list of annotation tools

21 best annotation tools for documents, video, and more

14 min read

December 4, 2023

Header_ Annotation and markup article (1)

In today’s data-driven world, annotation tools have become indispensable for various tasks, ranging from data labeling and content review to collaborative editing and research analysis. 

These tools empower users to add comments, highlights, and annotations to different types of content, enabling enhanced organization, collaboration, and understanding.

In this article, we will explore the top 21 annotation tools that cater to different purposes and domains.

What are annotation tools?

Annotation tools are software applications or platforms designed to facilitate the process of adding comments, highlights, or other annotations to various types of content. These tools play a crucial role in tasks such as data annotation, document review, collaborative editing, and research analysis. 

Annotation tools offer a range of features and functionalities that let users mark specific sections of text, images, videos, or documents for further context, explanation, or reference.

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Types of annotation tools

There are various different types of annotation tools on the market that cater to specific needs. 

Here’s a list of the seven most common types of annotation tools: 

  • Data annotation tools – these tools allow users to highlight, underline, strikethrough, or add comments to text-based content
  • Image annotation tools – annotate images by adding text, shapes, or drawings
  • Video annotation tools – annotate videos frame by frame, marking objects, actions, or events
  • Audio annotation tools – annotate audio files by adding markers, tags, or comments at specific timestamps or sections
  • Web annotation tools – annotate web pages or online documents, facilitating collaborative research and discussions
  • Document annotation tools – annotate academic papers, research, articles, and more
  • Code annotation tools – add comments, explanations, or documentation within source code files

Our shortlist of the top three annotation tools

Annotation tools can serve various different purposes. Here’s a closer look into the top three annotation tools that offer a comprehensive set of features from different fields of annotation.

1. Filestage – the best annotation tool for reviewing various file formats

Filestage is a review and feedback tool that makes it simple to collaborate on PDF documents, share videos , graphics, and marketing projects in general. 

It offers project managers a wide range of annotation and proofing features, which makes it easy for your internal and external key stakeholders to leave and discuss logical in-context feedback in real time.

A range of workflow features will make sure that you never miss a deadline or a game-changing suggestion. Next to custom pricing options, Filestage offers a Free plan – so you can check out the markup software at no risk.

And with the ability to integrate Filestage with Microsoft Teams, Jira, Adobe Premiere Pro, and a bunch of other tools, it fits in perfectly with your tool stack.

Key features

  • Collaborative video annotation – Filestage enables multiple stakeholders to review and annotate various file types, including images, videos , and documents, in a centralized platform
  • Review workflow management – It offers customizable workflows and task assignments, facilitating seamless collaboration and efficient annotation processes
  • Commenting and markup tools – Users can leave comments, draw annotations, and highlight specific areas within files to provide feedback and suggestions
  • Document version control – Filestage keeps track of file versions, allowing reviewers to compare changes and annotations over time
  • Approval and feedback management – It provides features for approving or rejecting annotations and consolidating feedback from multiple reviewers

2. Labelbox

labelbox

Labelbox is a popular data annotation platform that offers a wide range of annotation capabilities for images, videos, and text. It provides an intuitive interface for annotating and labeling data, along with powerful collaboration and project management features.

Labelbox supports various annotation types, including bounding boxes, polygons, keypoints, and semantic segmentation. It also offers automation features like active learning and model-assisted labeling.

  • Data annotation – Labelbox supports a range of annotation types, including bounding boxes, polygons, semantic segmentation, and keypoint annotations, for various data types such as images, videos, and text
  • Collaboration and review – It allows teams to collaborate on annotation projects, review annotations, and manage annotation quality control.
  • Automation and active learning – Labelbox offers features for automating certain annotation tasks using pre-trained models and implementing active learning techniques to reduce annotation efforts
  • Workflow management – users can create customized annotation workflows, assign tasks, and track the progress of annotation projects
  • Integration and deployment – Labelbox provides integration with popular machine learning frameworks and cloud storage platforms for seamless data management and model deployment

3. SuperAnnotate

SuperAnnotate

SuperAnnotate is a versatile and feature-rich data annotation platform that caters to image and video annotation needs. It offers a user-friendly interface with advanced annotation tools for bounding boxes, polygons, key points, and semantic segmentation.

SuperAnnotate provides powerful automation capabilities, including AI-assisted annotation and collaborative review workflows. It also offers integrations with popular machine-learning frameworks and platforms.

  • Advanced annotation tools – SuperAnnotate offers a wide range of annotation tools and techniques, including bounding boxes, polygons, keypoint detection, instance segmentation, and semantic segmentation
  • Collaboration and review – It enables real-time collaboration among team members, allowing them to review and validate annotations together
  • Quality control – SuperAnnotate provides tools for quality control, such as metrics calculation, consensus review, and inter-annotator agreement analysis
  • Automation and AI assistance – The platform uses AI to accelerate annotation tasks, improve annotation efficiency, and reduce human error
  • Project management and versioning – SuperAnnotate offers project management features, version control, and comparison tools for managing and tracking annotation projects

Best data annotation tools

Data annotation tools are software applications or platforms specifically designed to assist in the process of annotating or labeling data.

Here are the top two data annotation tools available on the market:

1. Amazon SageMaker Ground Truth

list of annotation tools

SageMaker Ground Truth is a data annotation tool provided by Amazon Web Services (AWS). It simplifies the process of creating high-quality labeled datasets for machine learning models. It offers a scalable and customizable platform for annotation tasks, supporting both automated and human-in-the-loop labeling.

SageMaker Ground Truth supports various annotation types, such as bounding boxes, semantic segmentation, and text classification. It integrates seamlessly with other AWS services, making it convenient for end-to-end machine learning workflows.

  • Customizable annotation workflows – It allows users to create custom annotation workflows tailored to specific data types and annotation tasks
  • Human-in-the-loop annotation – It combines automated annotation with human review, providing mechanisms to review and improve machine-generated annotations
  • Scalability – The tool is designed to handle large-scale annotation projects
  • Active learning – It supports active learning techniques to intelligently select samples for annotation and optimizes the annotation process
  • Integration with Amazon Services – It seamlessly integrates with other Amazon Web Services (AWS) products, such as Amazon Rekognition, for enhanced annotation capabilities

2. SuperAnnotate

SuperAnnotate is a versatile and feature-rich data annotation platform that caters to image and video annotation needs. It offers a user-friendly interface with advanced annotation tools for bounding boxes, polygons, keypoints, and semantic segmentation.

SuperAnnotate provides powerful automation capabilities, including AI-assisted annotation and collaborative review workflows. It also offers integrations with popular machine learning frameworks and platforms.

  • Advanced annotation tools
  • Collaboration and review
  • Quality control
  • Automation and AI assistance
  • Project management and versioning

Image annotation tools

Image annotation tools allow you to annotate images by adding bounding boxes, polygons, keypoints, or semantic segmentation masks to identify objects or regions of interest within the image.

Here are the two most popular image annotation tools:

1. RectLabel

list of annotation tools

RectLabel is a powerful visual object tagging tool specifically designed for object detection and localization tasks. It provides an intuitive interface with robust image annotations, including bounding boxes, polygons, lines, and keypoints.

RectLabel supports automatic annotation suggestions and offers features like image segmentation, instance tracking, and batch processing. It also integrates seamlessly with popular machine learning frameworks like TensorFlow and PyTorch.

  • Image annotation – RectLabel focuses on image annotation and offers tools for bounding box annotation, segmentation masks, keypoints, and text annotations
  • Object tracking – It provides features for object tracking within videos, allowing users to annotate objects across frames and track their movement
  • Collaboration and versioning – RectLabel supports collaboration among team members and keeps track of annotations and changes across versions of the data
  • Customizable annotation tools – Users can customize annotation tools and labels to match their specific annotation requirements and workflow
  • Export formats – RectLabel allows users to export annotations in various formats, including Pascal VOC, COCO, YOLO, and TensorFlow Object Detection API

2. VGG Image Annotator (VIA)

VGG-Image-Annotator-VIA-1

VIA is a versatile and open-source image annotation tool developed by the Visual Geometry Group at the University of Oxford. It supports annotation of various shapes, including points, polygons, rectangles, and ellipses.

VIA allows for annotating images individually or in batches, and it supports exporting annotations in multiple formats. It also offers customizability and extensibility, making it suitable for research and specialized annotation requirements.

  • Multi-format support – VIA supports a wide range of image formats, allowing users to annotate images in formats such as JPEG, PNG, and GIF
  • Multiple annotation types – It provides various annotation types, including bounding boxes, polygons, keypoints, and region descriptions, to annotate objects and regions of interest within images
  • Collaboration and version control – VIA supports collaborative annotation, allowing multiple users to work on the same set of images simultaneously
  • Annotation import/export – VIA allows users to import and export annotations in different formats, including JSON, CSV, and VIA project files
  • Customization and automation – VIA offers customization options, such as custom attribute definitions, and allows users to apply pre-trained models for automated annotation

Video annotation tools

As the name suggests, video annotation tools are used to add additional information on videos. This means that you can mark objects, track movements, or label frames.

Here are the two best annotation tools on the market:

1. Video Annotation Tool from Internet Curation (VATIC)

list of annotation tools

VATIC is a popular open-source video annotation tool developed by the Computer Vision Group at Stanford University. It offers an intuitive web-based interface for annotating videos frame by frame. Users can draw bounding boxes, track objects, and add labels or attributes to the annotated regions. VATIC supports collaboration, allowing multiple annotators to work on the same video simultaneously.

  • Video annotation capabilities – VATIC focuses specifically on video annotation, allowing users to annotate objects, track their movements, and draw on videos
  • Collaborative annotation – It supports collaborative annotation workflows, enabling multiple users to annotate videos and review annotations together.
  • Tracking and object identification – VATIC provides tools for object tracking, allowing users to annotate objects across frames and track their trajectories
  • Review and quality control – The tool offers review and validation features
  • Project management – VATIC includes project management features, such as task assignment and progress tracking

2. Computer Vision Annotation Tool (CVAT)

list of annotation tools

CVAT is a powerful open-source video annotation tool developed by Intel. It supports annotation for a wide range of tasks, including object detection, tracking, and segmentation. CVAT allows users to annotate videos by drawing bounding boxes, polygons, polylines, or points.

It offers collaborative annotation, synchronization across multiple annotators, and detailed annotation statistics. CVAT also provides automation features like interpolation, semi-automatic annotation, and inference integration.

  • Wide range of annotation types – CVAT supports diverse annotation types
  • Collaborative annotation and review – It enables real-time collaboration among team members, by collectively annotating and reviewing data
  • Automatic annotation – CVAT offers AI-based assistance to accelerate annotation tasks and reduce manual effort
  • Advanced video annotation features – CVAT provides comprehensive video annotation capabilities, including object tracking, video segmentation, and frame-level annotation
  • Integration and deployment –The tool allows integration with deep learning frameworks and supports exporting annotations in formats compatible with popular machine learning libraries

Audio annotation tools

When looking into different audio annotation tools, you should consider all the file formats it supports, and whether it’s designed for more complex annotation tasks.

Here are the top two audio annotation tools with comprehensive set of audio annotation features:

1. EUDICO Linguistic Annotator (ELAN)

list of annotation tools

ELAN is a powerful and widely used audio annotation tool developed by the Max Planck Institute for Psycholinguistics. It offers a comprehensive set of features for annotating audio and video recordings.

ELAN allows users to create and edit time-aligned annotations for multiple tiers, such as transcriptions, translations, and linguistic analysis. It supports various audio formats, offers advanced search and analysis capabilities, and is suitable for linguistic research and complex annotation tasks.

  • Linguistic annotation – ELAN is specifically designed for linguistic annotation, enabling users to annotate speech, language, and gesture data
  • Multi-layer Annotation – It supports multiple layers of annotation, allowing users to create and align annotations at different linguistic or temporal levels
  • Time-aligned annotations – ELAN provides precise time alignment for annotations
  • Metadata and transcription – The tool allows users to add metadata, transcriptions, and descriptive information to annotations, providing contextual information
  • Annotation visualization and analysis – ELAN offers visualization and analysis tools to explore and analyze linguistic annotations and their relationships

list of annotation tools

Praat is a versatile and popular tool for audio annotation and analysis. It provides a wide range of features for manipulating and annotating audio recordings. Praat allows users to create annotations, transcriptions, and phonetic labels at various time points.

It offers visualization tools, signal processing capabilities, and scripting options for advanced customization. Praat is widely used in phonetics, speech analysis, and linguistic research.

  • Speech and phonetics annotation – Praat is a specialized tool for speech and phonetics analysis, providing annotation features for audio recordings and linguistic analysis
  • Waveform and spectrogram visualization – It offers visualizations of audio waveforms and spectrograms to facilitate detailed analysis and annotation
  • Annotation types – Praat supports various annotation types, including segment labels, point labels, interval labels, and boundary labels, for precise annotation of speech data
  • Scripting and automation – The tool allows users to create scripts to automate repetitive annotation tasks or perform advanced analyses
  • Acoustic analysis – Praat allows users to extract and analyze acoustic features from speech data

3. Audacity

list of annotation tools

Although primarily known as an audio editing tool, Audacity also includes basic annotation features. It allows users to select specific regions of audio and add labels, comments, or markers.

While Audacity’s annotation capabilities may not be as extensive as dedicated annotation tools, it can be a convenient option for simple audio annotation tasks or when combined with its audio editing functionalities.

  • Audio recording and editing – Audacity allows users to record, import, and edit audio files with a range of editing capabilities, including cut, copy, paste, and apply effects
  • Multi-track editing – It supports multi-track editing, allowing users to work with multiple audio tracks simultaneously, adjust levels, and create complex audio compositions
  • Effects and plugins – audacity offers a wide range of built-in audio effects, such as reverb, equalization, and noise reduction
  • Spectrogram analysis – It provides a spectrogram view that displays the frequency and intensity of audio over time, allowing users to visualize and analyze audio characteristics
  • Cross-platform and open source – Audacity is a free and open-source tool available for Windows, macOS, and Linux, making it accessible to users across different operating systems

Web annotation tools

When it comes to annotating a web page, it’s best to turn to a specialized web annotation software.

Here are three great examples of web annotation tools:

1. Hypothesis

list of annotation tools

Hypothesis is a popular web annotation tool that allows users to annotate web pages and PDF documents. It offers a browser extension that enables users to highlight text, add comments, and create discussions within the context of the webpage. Hypothesis supports public and private annotations, collaboration features, and integration with learning management systems. It promotes open annotation standards and interoperability.

  • Web annotation – Hypothesis allows users to annotate web pages, documents, and digital content, making it easy to highlight, comment, and add notes directly on the content being viewed
  • Collaboration and discussion – It supports collaborative annotation, allowing users to engage in discussions, reply to annotations, and interact with other users’ annotations
  • Privacy and control – Hypothesis provides privacy controls, allowing users to choose whether their annotations are public or restricted to a specific group or audience
  • Integration with learning management systems (LMS) – It offers integration with popular LMS platforms, allowing educators to incorporate collaborative annotation activities into their online courses
  • API and customization – Hypothesis provides an API for developers to integrate annotation capabilities into their own applications or websites

list of annotation tools

Diigo is a versatile web annotation and bookmarking tool that enables users to highlight, bookmark, and add sticky notes to web pages. It offers browser extensions for easy annotation and organization of online content.

Diigo supports social annotation functionality, sharing and collaboration options, and the ability to create private or public annotation groups. It also provides a personal library for managing annotated resources.

  • Web highlighting and sticky notes – You can add sticky notes directly on web pages to mark important information and save personal notes
  • Webpage bookmarking and organization – It allows users to bookmark webpages and save them in an organized manner with tags, folders, and search functionality for easy retrieval
  • Social annotation and collaboration – Diigo supports social annotation, allowing users to share their highlights, annotations, and bookmarks with others
  • Webpage archiving – It provides a webpage archiving feature, allowing users to capture and save a copy of webpages, even if the original page is modified or removed
  • Knowledge sharing and discovery – Diigo offers a platform for users to discover and explore content shared by others, expanding their knowledge base and staying updated with new information

list of annotation tools

Kami is a web-based annotation tool designed for collaboration and document annotation. It supports annotating PDF files , Google Drive documents, and web pages. Kami allows users to highlight text, add comments, draw shapes, and insert images within documents.

It offers real-time collaboration, sharing options, and integration with Google Classroom and learning management systems.

  • PDF annotation and markup – Kami allows users to annotate and markup PDF documents with tools for highlighting, underlining, adding comments , and drawing shapes
  • Collaboration and real-time editing – It supports real-time collaboration, enabling multiple users to work on the same document simultaneously and view each other’s annotations in real time
  • Document organization and management – Kami provides features for organizing and managing PDF documents, including file merging, splitting, and rearranging pages
  • Integration with Google Drive and Classroom – It offers integrations that allow you to easy access your files and create streamlined collaboration within the Google ecosystem
  • Accessibility and integration with assistive technology – Kami offers accessibility features, including support for screen readers and integration with other tools to enhance the accessibility of PDF documents

Document annotation tools

Unlike any AI and natural language processing annotation tools, document annotation tools have a completely different purpose. They allow you to mark certain areas of your document and leave in-context feedback on top.

Here’s a look into some of the best document annotation tools:

1. Adobe Acrobat

adobe-acrobat-reader

Adobe Acrobat is a widely used and feature-rich document annotation tool. It offers a comprehensive set of annotation tools, including highlighting, underlining, strikethrough, adding comments, and drawing shapes. Adobe Acrobat allows for collaboration and review workflows, supports form filling, and offers advanced features like redaction and document security. It is compatible with PDF documents and is available for both desktop and mobile devices.

  • Comprehensive PDF editing features
  • PDF form creation and fillable forms
  • Document security and protection
  • OCR (Optical Character Recognition)
  • Integration with Adobe Creative Cloud

list of annotation tools

Xodo is a versatile and user-friendly document annotation tool available on various platforms, including Windows, Mac, Android, and iOS. It provides a range of annotation tools, such as highlighting, underlining, adding text, shapes, and signatures. Xodo offers seamless syncing across devices, supports collaboration, and allows for document signing. It is particularly popular for its smooth and responsive user interface.

  • PDF annotation and markup
  • Collaboration and syncing
  • Document organization and management
  • PDF form filling and signing
  • Cloud storage integration

3. Nitro PDF

list of annotation tools

Nitro PDF is a robust document annotation tool that offers a wide array of annotation options. It enables users to highlight text, add comments, draw shapes, and apply stamps or signatures.

Nitro PDF supports collaboration and version control, and it provides advanced features like OCR (Optical Character Recognition) and PDF form creation. It is available for both Windows and Mac platforms.

  • PDF creation and conversion
  • PDF editing and annotation
  • Collaboration and review features
  • Document signing and security
  • OCR capabilities to convert scanned documents into searchable and editable text

Code annotation tools

If you’re looking to review a code, then these are the tools for you. These tools offer features like code highlighting, commenting, and version control integration, making it easier to leave feedback and collaborate on source code.

Here are the two best code annotation software:

1. Visual Studio Code (VS Code)

list of annotation tools

VS Code is a popular and feature-rich code editor that offers built-in support for code annotation. It provides features like line commenting, block commenting, and the ability to add descriptive comments within code.

VS Code also supports extensions that enhance code annotation capabilities, such as adding tags, generating API documentation, or creating interactive code snippets.

  • Rich code editing experience with features like syntax highlighting, auto-completion, and intelligent code suggestions
  • Extensibility and customization options to tailor the environment to their specific needs
  • Version control integration with version control systems like Git, providing tools for managing code repositories, tracking changes, and resolving conflicts
  • Built-in debugging capabilities for various programming languages
  • It includes an integrated terminal for executing commands and a task runner for automating repetitive tasks within the development workflow

list of annotation tools

Eclipse is a widely used integrated development environment (IDE) that includes comprehensive code annotation features.

It supports commenting at the line, block, or method level and provides options to generate Javadoc comments for Java code. Eclipse also offers refactoring tools and code analysis features that aid in maintaining code quality and documentation.

  • Robust Java Development environment – Eclipse is renowned for its strong support for Java development, offering features like code completion, refactoring tools, and debugging capabilities
  • Plugin Ecosystem – It boasts a vast ecosystem of plugins and extensions, allowing users to enhance Eclipse’s functionality for various programming languages and development purposes
  • Project management and tools integration
  • Debugging and profiling capabilities – It allows users to identify and resolve issues in their code and optimize performance with powerful debugging capabilities
  • Collaboration and version control – Eclipse supports integration with version control systems like Git and provides features for collaborative development, code reviews, and team coordination

3. JetBrains IntelliJ IDEA

list of annotation tools

IntelliJ IDEA is a powerful IDE known for its extensive code annotation capabilities. It provides various features to add comments, generate Javadoc, and create code documentation. IntelliJ IDEA supports different programming languages, including Java, Kotlin, Python, and JavaScript.

It offers code inspections, auto-completion, and other intelligent features that facilitate effective code annotation.

  • Intelligent code editor with features like code completion, intelligent code analysis, and smart suggestions
  • Advanced refactoring tools that assist in restructuring code and improving its quality
  • Built-in version control with systems like Git, Mercurial, and Subversion
  • Productivity boosters, including features like code generation, live templates, quick-fix suggestions, and customizable code inspections to catch potential errors and improve coding speed
  • Robust plugin ecosystem

Annotate your files for free with Filestage

Filestage is a free annotation tool that allows you to seamlessly leave feedback on top of PDF documents, videos, graphics, and marketing projects in general. 

With Filestage, you can create a smooth annotation process by allowing all important stakeholders to leave feedback and review files.

Filestage’s free version is designed for freelancers and smaller teams and includes:

  • Unlimited files, versions, and reviewers
  • 2 active projects
  • 2 GB of storage
  • 2 review steps
  • Support for all file formats
  • Integrations
  • Due dates and reminders
  • Awaiting my review

Final thoughts

Various annotation tools are developed to cater to a wide range of purposes and industries. Whether you’re annotating images, videos, audio, code, or text content, these 21 annotation tools discussed in this article offer valuable features and capabilities to streamline your annotation workflows.If you’d like to see how Filestage can help you annotate various file formats and streamline your annotation process, make sure to try it for free →

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Top 8 Annotation Tools (Features, Pros, Cons, Pricing)

Erica Chappell

Managing Editor

February 13, 2024

Looking for the best annotation software ?

Whether it’s business professionals or teachers, when asked to choose between scribbled notes and a well-labeled document , we’re sure they’ll pick the latter. 

Annotation Software

Annotation software marks a document or piece of content with anything a user wants, like text , an image , shapes, signatures, and more!

A color-coded and properly labeled document helps present your thoughts clearly.  

And annotation tools help you do just that.

But with hundreds of such software in the market, which one should you pick?

In this article, we’ll cover what an annotation tool is and highlight eight popular annotation software available today. We’ll go over their key features, pros, cons, and pricing to help you pick the best one.

What Is an Annotation Tool? 

2. filestage, 4. annotate, 5. pdf annotator, 6. drawboard projects, bonus tool: markup hero.

Let’s get started!

An annotation tool is a text or drawing tool that helps you add information to text, an image, a database , or any other piece of content.

But that’s not all.

These tools can also be used on various formats such as a whiteboard or PowerPoint presentation , to annotate the selected content. And we’re not just talking about business or education content.

Annotation software is even used in fields like gene ontology ! 

For example, Genome annotation or DNA annotation tools help annotate the gene location in a genome .

Not the DNA that you’re thinking of!

Click on the above gif to know what we meant.

And that folks, was an example of web annotation . 

What’s web annotation ?

A web annotation is the addition of information to resources present on web pages. 

For example, adding tags to GIFs of Korean boy bands in a blog post is an example of a web annotation . 

Now, while we’re on the topic of examples…

What are annotations examples?

Since annotation is essentially just marking and adding tags to sections of content, there are tons of examples out there.

Here are a few common ones:

  • Adding informative tags to an unlabeled dataset ( data labeling ) like a spreadsheet
  • Using a line tool or a pencil tool to draw on the screen during a presentation
  • Highlighting important text in a shared document with a highlighter tool
  • Labeling your lunch as “do not touch” when you store it in the company fridge

(Well, maybe not that last one)

But if you think annotation is just about adding manual notes to the content, think again. 

What are 3 types of annotations?

  • Manual annotation : the manual annotation type is exactly what it sounds like – it covers the process of manually annotating a piece of content
  • Screen annotation : the annotation of a desktop screen with the help of a screen marker software is known as screen annotation (you commonly use these during Zoom meetings and other video conferences)
  • Collaborative annotation : collaborative annotation is a teaching strategy to help students develop critical thinking, reading, and writing. 

Top 8 Annotation Tools in 2022

Here are the top eight annotation software that’ll make annotating your projects a cakewalk:

ClickUp is the world’s #1 project management tool .

But wait… you’re looking for annotation tools.

Why would a project management tool help?

That’s because ClickUp has advanced built-in annotation features to help you with all your annotation needs. And since it’s a comprehensive project management solution, you can use it for more than just annotation tasks !

From productivity monitoring to resource management , ClickUp can handle anything you throw at it!

Let’s see why ClickUp is the best annotation tool available today:

How do you annotate?

ClickUp’s Annotation feature supports PDF file and image annotation (.png, .gif, .jpeg, .webp).

You can easily add comments to task attachments in 4 simple steps:

  • Open the desired attachment within a task
  • Click “Add comments” in the upper right of the preview window
  • Click on the attachment preview wherever you want to add a comment
  • Add comments and assign them to anyone if you want them to take immediate action

Voila! 

Your added comment will now appear in the comments tab next to the attachment. Now the assigned comment will appear in the assignee’s Home tab as well as in the task’s comments section.

assigned comment for annotation

What’s more?

For added flexibility, you can even use ClickUp’s Chrome Extension for annotation and proofing documents. You can use the Chrome extension to markup images, upload them as a task attachment or add comments to the attachment — with ClickUp everything is possible!

Other key ClickUp features

  • Docs : easily create, share, and store project or company-related documents with your virtual or in-house team 
  • Comments : each ClickUp task offers a dedicated comment section that you can use to easily discuss and share any relevant file or links. You can even tag team members to ensure your comments aren’t overlooked.
  • Collaboration Detection : know when your teammates are simultaneously adding a comment or viewing a task in real-time. This can be extremely useful to overcome some common virtual team challenges you’d face in remote project management .
  • Notepad : a free space to jot down your thoughts and ideas quickly
  • Mind Maps : create free form mind maps to plan and organize your project-related ideas

ClickUp pros

  • User-friendly interface
  • Offers a feature-rich  free version
  • Can easily assign tasks to team members
  • Easy task management with the drag-and-drop feature 
  • Efficient resource management with more than one assignee for a specific task 
  • Manage and track your project timeline with intuitive Gantt Charts
  • Custom Access Rights help to share your projects with clients without compromising privacy
  • Can quickly find all your project resources with the Smart Search feature
  • Attempt project tasks in the right order with Task Dependencies
  • Agile dashboards to measure your project progress with charts like Velocity , Burnup , Burndown , and Cumulative Flow  
  • Can customize how you receive project notifications
  • Offers app integrations with various third-party apps for seamless project management
  • Available as web browser , desktop, and mobile apps

ClickUp cons

  • No white-labeling available (can’t remove brand icon )
  • Can’t export Dashboards

But don’t worry, at ClickUp, we’re constantly working to overcome such minor flaws.

Click here to learn more about what’s cooking in the ClickUp kitchen.

ClickUp pricing

ClickUp has three pricing options:

  • Free Forever Plan : includes unlimited projects and members + 100MB of cloud storage space 
  • Unlimited Plan ($7/user per month) : supports unlimited projects and members + forms + unlimited number of integration + advanced reporting + unlimited Gantt charts
  • Business Plan ($12/user per month) : includes unlimited projects and members + unlimited Proofing + timesheets + two-factor authentication + sprint points + Google single sign-on + custom exporting

filestage

Filestage is a good proofing and annotation tool to help you annotate a PDF file as well as videos and audios. 

Filestage key features

  • Add annotation to comments with markup tools
  • Add attachments to include reference files in comments
  • Use their dashboards to manage all review-related workflows
  • Threaded comments facilitate clear discussions
  • Easily review audio and video files with time codes

Filestage pros

  • Can easily track file status during the review process
  • Can add due dates to avoid missing deadlines
  • Easily integrates with third-party apps
  • Can add attachments in comments to enhance collaboration

Filestage cons

  • Cannot undo an approved annotation action
  • No support for web annotation
  • Cannot assign tasks to team members

Filestage pricing

Filestage offers three pricing tiers:

  • Starter ($99/month) : includes 15 active projects + basic project structure + basic support 
  • Pro ($249/month) : includes 35 active projects + advanced project structure + priority support + 10 team members + project template
  • Enterprise (custom pricing) : supports more projects + custom workflows + more team members

prodigy

Prodigy is a data annotation tool that creates evaluation and training data for machine learning models. And while this data tool could act as a prodigy for annotation, it’s not what’d you call affordable for most teams.

Prodigy key features

  • Offers text annotation for any script or language
  • Allows text classification with multiple categories
  • Annotate NLP (Natural Language Processing) relations to label your data
  • Image annotation tool for tasks like object detection, image classification, and segmentation 

Prodigy pros

  • Suitable for novice users
  • Along with text and image annotation , can annotate audio clips as well
  • Evaluate any model with randomized A/B testing
  • Offers live demo and documentation for ease of use

Prodigy co ns 

  • Supports collaboration annotation only for small teams
  • You’ll need a separate tool for managing projects
  • Doesn’t offer advanced semantic segmentation feature

Prodigy pricing

Prodigy comes with two pricing plans:

  • Personal ($390/lifetime license) : offers a lifetime license + unlimited projects + unlimited annotator  
  • Company ($490/seat) : includes “personal” features + flexible and transferable floating licenses

annotate

Annotate is a document collaboration platform that helps make the document annotation process a breeze, from the first draft to the final sign-off. However, if you’re looking to annotate on the move, this might not be the tool for you.

Annotate key features

  • Perform text or image annotation with the help of figures like text box , polygon or freehand notes
  • Easily create notes within a document and assign them to team members 
  • Communicate over document and workspace chat while reviewing documents
  • Draft your own documents and export as Word or PDF documents

Annotate pros

  • Supports custom integrations with third-party apps
  • Ability to create workflows within documents
  • Restricted access for external collaborators

Annotate cons

  • Doesn’t offer Android and iOS mobile apps
  • No support for task and project management
  • No calendar feature to manage your work schedule

Annotate pricing

Annotate offers three pricing plans:

  • Free : supports up to 3 users + text annotation + document workspaces + full text search + in document collaboration 
  • Standard (contact the sales team) : minimum 3 users + includes “Free” features + freehand annotation + notifications + document metadata and tagging
  • Enterprise (contact the sales team) : includes “Standard” features + unlimited storage + unlimited users + document comparisons

pdf annotator

PDF Annotator is a PDF annotation tool that lets you add notes and annotations to your PDF documents . This annotation software enables you to add comments, signatures, and designs,  making PDF annotation super easy.

PDF Annotator key features

  • Can type your comments right into the document
  • Freehand annotations with the Pen Tool
  • Add stamps, signatures and page numbers to the document
  • You can easily add markup and insert images 
  • Import and export pdf annotation across different document versions

PDF Annotator pros

  • Offers an embedded image editor
  • Convert your document to any format like doc, xls or ppt
  • Can extract, move or delete selected pages from a document

PDF Annotator cons

  • Adobe DRM protected documents cannot be opened
  • Only available for Windows operating systems
  • Cannot annotate digital signature protected documents 

PDF Annotator pricing

PDF Annotator offers 3 buying options (prices vary for 4 users and above): 

  • Annotator License: $69.95/ user
  • Student License : $39.95/ user
  • Faculty License: $69.95/ user

drawboard tool

Drawboard Projects is a PDF markup software tool that offers a wide range of markup features like text tool and polygon tool . You can even annotate your documents with digital ink. However, if you’re looking to organize your annotations easily, you might want to look elsewhere.

Drawboard Projects key features

  • Markup any PDF file or drawing
  • Pin an issue to a specific area on drawings to provide context
  • Offers a time-restricted review link for guest access
  • Add tags to a file to enhance the searchability 

Drawboard Projects pros

  • Collaborative markup and text annotation
  • Can easily assign and track issue progress
  • Integration with apps like Google drive and Procore
  • Offers offline documentation support

Drawboard Projects cons

  • Cannot organize projects into folders
  • Only offers Windows 10 and iOS apps
  • Integration with limited applications

Drawboard Projects pricing

  • Basic ($24/ user per month) : supports up to 1000 drawings or documents + unlimited projects + third party app integrations 
  • Standard ($44/ user per month) : offers unlimited drawings or documents + unlimited projects + email support + third party app integrations
  • Enterprise (contact Drawboard team) : includes “Standard” features + federated access logins (SSO) + priority support

doccano

Doccano is an open-source text annotation tool . With its simple user interface, you can easily upload your desired dataset and start annotating.

Doccano key features

  • Ability to perform sentiment analysis on text data
  • Offers text classification that can be exported as plain text, CSV, JSON formats
  • Supports data annotation feature of sequence labeling
  • Sequence to sequence learning for training data models

Doccano pros

  • Simple, user-friendly  interface
  • Auto label with trained machine learning models
  • Can annotate text of any language
  • Open source annotation software

Doccano cons

  • Can lag and become unresponsive at times
  • Can shuffle the annotated pieces only when self-hosted
  • Requires coding-heavy setup

Doccano pricing

As Doccano is open source , it’s a free annotation tool .

ink2go

Ink2Go is a simple screen recording and screen annotation software . Its user-friendliness makes it an excellent candidate for any live presentation or seminars.

Ink2Go key features

  • Write, draw or highlight any content on desktop or apps like Sticky Note
  • Screenshot , screen capture or record your desktop with the added annotations
  • Offers a built-in digital whiteboard or blackboard for better user experience
  • Annotate across multiple pages simultaneously

Ink2Go pros

  • Supports webcam for presentation and seminars
  • Can annotate on extended or shared screen
  • Floating drawing tool bar with the ability to align both horizontally and vertically
  • Multiple pens, highlighters, and shapes like a rectangle for high-quality presentations

Ink2Go cons

  • Limited toolbar items
  • Irregular software version releases
  • No live customer support

Ink2Go pricing

Ink2Go offers both Windows and Mac apps with a 15-day free trial. However, a single license costs $19.99.

list of annotation tools

Markup Hero is a screenshot and annotation tool that is web based, easy to use, and includes a bunch of helpful markup controls including: callouts, highlight, signature, crop, pen, shapes, colors, blur, and more. In Markup Hero, annotations are not flattened so you can edit you markup forever. Organize annotations with collections, search and naming, and share instantly with a link or copy to clipboard.

Markup Hero key features

  • Always editable markups
  • Multi-page markups and insert images
  • Share links with permissions
  • Chrome Extension for scrolling screenshots

Markup Hero pros

  • Annotated images, PDF’s, Google Docs, and Websites
  • Works on any device including mobile
  • Good for screenshots and annotations
  • Easily embeddable into Slack, social and even ClickUp tickets

Markup Hero cons

  • No video capture
  • No team accounts (yet)
  • No custom domains

Markup Hero pricing

Markup Hero has a forever free plan with limited monthly annotations and you try the tool without even making an account. Unlimited markups and full features on the Super Hero plan is only $4.00 per month.

A well placed, detailed annotation is one of the easiest ways to add value to your content. It’s what can transform a boring piece of data into something as exciting as this:

Well, maybe not that exciting.

But you get the point.

Luckily, choosing an annotation software that fits your needs doesn’t have to be as difficult as perfecting English grammar!

Just go through the 8 options for annotation tools we covered here for a head start. 

And while each of these tools is great, ClickUp smashes it out of the park with its powerful features that help you proof your documents easily. 

From image and PDF proofing to a feature-rich  Forever Free plan, this versatile annotation software hits the home run.

Sign up for ClickUp today to present your perfectly annotated ideas to the world! 

Questions? Comments? Visit our Help Center for support.

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Data Annotation Tools for Machine Learning (Evolving Guide)

Choosing the Best Data Annotation Tool for Your Project

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The data annotation tools you use to enrich your data for training and deploying machine learning models can determine success or failure for your AI project. Your tools play an important role in whether you can create a high-performing model that powers a disruptive solution or solves a painful, expensive problem - or end up investing time and resources on a failed experiment.

Choosing your tool may not be a fast or easy decision. The data annotation tool ecosystem is changing quickly as more providers offer options for an increasingly diverse array of use cases. Tooling advancements happen by the month, sometimes by the week. These changes bring improvements to existing tools and new tools for emerging use cases.

The challenge is thinking strategically about your tooling needs now and into the future. New tools, more advanced features, and changes in options, such as storage and security, make your tooling choices more complex. And, an increasingly competitive marketplace makes it challenging to discern hype from real value.

We’ve called this an evolving guide because we will update it regularly to reflect changes in the data annotation tool ecosystem. So be sure to check back regularly for new information, and you can bookmark this page.

The Outsourcers' Guide to Quality

Read the full guide below, or download a PDF version of the guide you can reference later.

In this guide, we’ll cover data annotation tools for computer vision and NLP (natural language processing) for supervised learning .

First, we’ll explain the idea of data annotation tools in more detail, introducing you to key terms and concepts. Next, we will explore the pros and cons of building your own tool versus purchasing a commercially available tool or leveraging open source options.

We’ll give you considerations for choosing your tool and share our short list of the best data annotation tools available. You’ll also get a short list of critical questions to ask your tool provider.

Table of Contents

Introduction: will this guide be helpful to me, the basics: data annotation tools and machine learning, a critical choice: build vs. buy, how to choose a data annotation tool, the best data annotation tools: commercial and open source, iteration & evolution: changing data annotation needs, new tools, questions to ask your data annotation tool provider, tool agnostic: the cloudfactory advantage.

  • Introduction
  • Build vs. Buy
  • How to Choose
  • Best Data Annotation Tools
  • Iteration & Evolution
  • Questions to Ask
  • CloudFactory Advantage

This guide will be helpful if :

  • You are beginning a machine learning project and have data you want to clean and annotate to train, test, and validate your model.
  • You are working with a new data type and need to understand the best tools available for annotating that data.
  • Your data annotation needs have evolved (e.g., you need to add features to your annotation) and want to learn about tools that can handle what you’re doing today and what you’re adding to your process.
  • You are in the production stage and must verify models using human-in-the-loop .

What’s data annotation?

In machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict. You are marking - labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize. Once your model is deployed, you want it to recognize those features on its own and make a decision or take some action as a result.

Annotated data reveals features that will train your algorithms to identify the same features in data that has not been annotated. Data annotation is used in supervised learning and hybrid, or semi-supervised, machine learning models that involve supervised learning.

What’s a data annotation tool?

A data annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to annotate production-grade training data for machine learning. While some organizations take a do-it-yourself approach and build their own tools, there are many data annotation tools available via open source or freeware.

They are also offered commercially, for lease and purchase. Data annotation tools are generally designed to be used with specific types of data, such as image, video, text, audio, spreadsheet, or sensor data. They also offer different deployment models, including on-premise, container, SaaS (cloud), and Kubernetes.

Data annotation tools have these key elements: They can be used to annotate many data types, including text, image, video, audio, time-series, and sensor data. They support annotation for 2-D, 3-D, video, audio, transcription, and text. You can buy a commercially-available data annotation tool, you can take a do-it-yourself approach and build your own, or you can use open source or freeware to create and tailor a data annotation tool for your use case.

6 Important Data Annotation Tool Features

1) dataset management.

Annotation begins and ends with a comprehensive way of managing the dataset you plan to annotate. As a critical part of your workflow, you need to ensure that the tool you are considering will actually import and support the high volume of data and file types you need to label. This includes searching, filtering, sorting, cloning, and merging of datasets. 

Different tools can save the output of annotations in different ways, so you’ll need to make sure the tool will meet your team’s output requirements. Finally, your annotated data must be stored somewhere. Most tools will support local and network storage, but cloud storage - especially your preferred cloud vendor - can be hit or miss, so confirm support-file storage targets.

2) Annotation methods

This is obviously the core feature of data annotation tools - the methods and capabilities to apply labels to your data. But not all tools are created equal in this regard. Many tools are narrowly optimized to focus on specific types of labeling, while others offer a broad mix of tools to enable various types of use cases.

Nearly all offer some type of data or document classification to guide how you identify and sort your data. Depending on your current and anticipated future needs, you may wish to focus on specialists or go with a more general platform. The common types of annotation capabilities provided by data annotation tools include building and managing ontologies or guidelines, such as label maps, classes, attributes, and specific annotation types.

Here are just a few examples:

  • Image or video: Bounding boxes, polygons, polylines, classification, 2-D and 3-D points, or segmentation (semantic or instance), tracking, transcription, interpolation, or transcription.
  • Text: Transcription, sentiment analysis, net entity relationships (NER), parts of speech (POS), dependency resolution, or coreference resolution.
  • Audio: Audio labeling, audio to text, tagging, time labeling

An emerging feature in many data annotation tools is automation, or auto-labeling . Using AI, many tools will assist your human labelers to improve their annotations (e.g. automatically convert a four-point bounding box to a polygon), or even automatically annotate your data without a human touch. Additionally, some tools can learn from the actions taken by your human annotators, to improve auto-labeling accuracy.

Some annotation tasks are ripe for automation. For example, if you use pre-annotation to tag images, a team of data labelers can determine whether to resize or delete a bounding box. This can shave time off the process for a team that needs images annotated at pixel-level segmentation. Still, there will always be exceptions, edge cases, and errors with automated annotations, so it is critical to include a human-in-the-loop approach for both quality control and exception handling.

Automation also can refer to the availability of developer interfaces to run the automations. That is, an application programming interface (API) and software development kit (SDK) that allow access to and interaction with the data.

3) Data quality control 

The performance of your machine learning and AI models will only be as good as your data. Data annotation tools can help manage the quality control (QC) and verification process. Ideally, the tool will have embedded QC within the annotation process itself.

For example, real-time feedback and initiating issue tracking during annotation is important. Additionally, workflow processes such as labeling consensus, may be supported. Many tools will provide a quality dashboard to help managers view and track quality issues, and assign QC tasks back out to the core annotation team or to a specialized QC team.

4) Workforce management

Every data annotation tool is meant to be used by a human workforce - even those tools that may lead with an AI-based automation feature. You still need humans to handle exceptions and quality assurance as noted before. As such, leading tools will offer workforce management capabilities, such as task assignment and productivity analytics measuring time spent on each task or sub-task.

Your data labeling workforce provider may bring their own technology to analyze data that is associated with quality work. They may use technology, such as webcams, screenshots, inactivity timers, and clickstream data to identify how they can support workers in delivering quality data annotation.

Most importantly, your workforce must be able to work with and learn the tool you plan to use. Further, your workforce provider should be able to monitor worker performance and work quality and accuracy . It’s even better when they offer you direct visibility, such as a dashboard view, into the productivity of your outsourced workforce and the quality of the work performed.

5) Security

Whether annotating sensitive protected personal information (PPI) or your own valuable intellectual property (IP), you want to make sure that your data remains secure.  Tools should limit an annotator’s viewing rights to data not assigned to her, and prevent data downloads. Depending on how the tool is deployed, via cloud or on-premise, a data annotation tool may offer secure file access (e.g., VPN).

For use cases that fall under regulatory compliance requirements, many tools will also log a record of annotation details, such as date, time, and the annotation author. However, if you are subject to HIPAA, SOC 1, SOC 2, PCI DSS, or SSAE 16 regulations, it is important to carefully evaluate whether your data annotation tool partner can help you maintain compliance.

6) Integrated labeling services

As mentioned earlier, every tool requires a human workforce to annotate data, and the people and technology elements of data annotation are equally important. As such, many data annotation tool providers offer a workforce network to provide annotation as a service. The tool provider either recruits the workers or provides access to them via partnerships with workforce providers.

While this feature makes for convenience, any workforce skill and capability should be evaluated separately from the tool capability itself. The key here is that any data annotation tool should offer the flexibility to use the tool vendor’s workforce or the workforce of your choice, such as a group of employees or a skilled, professionally managed data annotation team.

6 key features for data annotation tools

Download the PDF version here

Just a few years ago, there weren’t many data annotation tools available to buy. Most early movers had to use what was available via open source or build their own tools if they wanted to apply AI to solve a painful business problem or create a disruptive product.

Starting in about 2018, a wave of commercial data annotation tools became available, offering full-featured, complete-workflow commercial tools for data labeling. The emergence of these third-party, professionally developed tools began to force a discussion within data science and AI project teams around whether to continue to take a DIY approach and build their own tools or purchase one. And if the answer was to purchase a data annotation tool, they still needed to decide how to select the right tool for their project.

When to build your own data annotation tool

Even though there are third-party tools available to purchase, it may still make business sense to build a data annotation tool. Building your own tool provides you with the ultimate level of control - from the end-to-end workflow of the annotation process, to the type of data you can label and the resulting outputs.

And, as you continue to iterate your business processes and your machine learning models, you can make changes quickly, using your own developers and setting your own priorities. You also can apply technical controls to meet your company’s unique security requirements. And finally, an organization may want to include all of their AI tooling in their intellectual property, and building a data annotation tool internally allows them to do that.

However, when you’re building a tool, you often face many unknowns at the beginning, and the scope of tool requirements can quickly shift and evolve, causing teams to lose time. There is also the additional overhead of standing up the infrastructure needed to develop and run the tooling, as well as development resources required to maintain the data annotation tool.

When to buy a data annotation tool

Generally, buying a tool that is commercially available can be less expensive because you avoid the upfront development and ongoing direct support expenses. This allows you to focus your time and resources on your core project:

  • Without the distraction of supporting and expanding features and capabilities for an in-house tool that is custom-built; and
  • Without bearing the ongoing burden of funding the tool to ensure its continued success.

Buying an existing data annotation tool can accelerate your project timeline, enabling you to get started more quickly with an enterprise-ready, tested data labeling tool. Additionally, tooling vendors work with many different customers and can incorporate industry best practices into their data annotation tools. Finally, when it comes to features, you can usually configure a commercial tool to meet your needs, and there are more than one of these kinds of tools available for any data annotation workload.

Of course, a third-party data annotation tool is not typically built with your specific use case or workflow in mind, so you may sacrifice some level of control and customization.  And as your project or product evolves, you may find that your data annotation tool requirements change over time. If the tool you originally bought doesn’t support your new requirements, you will need to build or buy integrations or separate tools to meet your new needs.

The open source option for data annotation tools

There are open source data annotation tools available. You can use an open source tool and support it yourself, or use it to jump-start your own build effort. There are many open source projects for tooling related to image, video, natural language processing, and transcription, and such a tool can be a great option for a one-time project.

But often an open source tool will present challenges when you try to scale your project into production, as these tools are typically designed around a single user and offer poor or insufficient workflow options for a team of data labelers. Additionally, you need to have the technical expertise on hand to deploy and maintain the tool. Many people are lured by open source being “free” and forget to factor in the total cost of ownership - the time and expense required to develop the workflows, workforce management, and quality assurance management that are necessary and inherently present in commercial data annotation tools.

Growth stage as an indicator for buy vs. build

Another helpful way to look at the build versus buy question is to consider your stage of organizational growth.

  • Start: In the early stages of growth, freeware or open source data annotation tools can make sense if you have development resources and you want to build your own tool. You also could choose a workforce that provides a data annotation tool. But be careful not to unnecessarily tie your data annotation tool to your workforce; you’ll want the flexibility to make changes later.
  • Scale: If you’re at the growth stage, you might want the ability to customize commercial data annotation tools, and you can do that with little to no development resources. If you build, you’re going to need to allocate resources to maintain and improve your tool. Keep in mind to consider existing storage and, if you use a cloud vendor, make sure they can work with your requirements.
  • Sustain: When you’re operating at scale, it’s likely to be important for you to have control, enhanced data security, or the agility to make changes, such as feature enhancements. In that case, open source tools that are self-built and managed might be your best bet.

When you are looking for a data annotation tool, an important consideration is the growth stage of your organization. In the early stages of growth, open source or crowdsourcing make sense. At the growth stage, consider commercial data annotation tools or building your own. At scale, you might want the control, enhanced data security, or agility you get from building your own data annotation tool.

There is a lot to consider in the build vs buy equation. If, after considering all of the factors, you conclude that the time and expense is not worth a DIY approach and the potential gain of customization and retaining IP, then the next decision you will need to make is about which commercial tool you choose to purchase. In this section we will explore some of those considerations.

1) What is your use case?

First and foremost, the type of data you want to annotate and your business processes for doing the work will influence your tool choice. There are tools for labeling text, image, and video. Some image labeling tools also have video labeling capabilities.

Of note, more and more data annotation tool providers are realizing they want to do more than provide a singular tool - they want to provide a holistic technology platform for data annotation for machine learning. A simple data annotation tool provides features that make it easy to enrich the data. A platform provides an environment that supports the data annotation and AI development process.

A platform may include features such as multiple annotation options (e.g., 2-D, 3-D, audio, text), more than one storage option (e.g., local, network, cloud), or quality control workflow. It also may be able to accept pre-annotated data or may include embedded neural networks that learn from manual annotations made using the platform. Considering a platform may be helpful if you anticipate your project or product needs evolving significantly over time, as a platform may provide greater flexibility in the future.

2) How will you manage quality control requirements?

How you want to measure and control quality is also an important consideration for your data annotation tool. Many commercially-available tools have quality control (QC) features built-in that can review, provide feedback, and correct tasks. For example, QC options might include:

  • Consensus - Annotator agreement determines quality. For example, when annotators disagree on an edge case, the task is passed to a third annotator or more until a percentage of certainty is reached. Feedback can be provided to the workforce to learn how to correctly annotate those edge cases.
  • Gold standard - The correct answer is known. The tool measures quality based on correct and incorrect tasks.
  • Sample review - The tools reviews a random sample of completed tasks for accuracy.
  • Intersection over union (IoU) - This is a consensus model used in object detection within images. It compares your hand-annotated, ground-truth images with the annotations your model predicts.

Some tools can even automate a portion of your QC. However, whenever you are using automation for a portion of your data labeling process, you will need people to perform QC on that work. For example, optical character recognition (OCR) software has an error rate of 1% to 3% per character. On a page with 1,800 characters, that’s 18-54 errors. For a 300-page book, that’s 5,400-16,200 errors. You will want a process that includes a QC layer performed by skilled labelers with context and domain expertise.

3) Who will be using the tool?

An often overlooked aspect of tool selection is workforce. Whether your data is annotated by employees or contractors, crowdsourcing, or an outsourcing provider, your workforce will need access to and training to use your data annotation tool, with specific task instructions unique to your use case. Make sure you take into account the answers to these questions:

  • Do you have access to a workforce that has pre-existing knowledge of viable commercial tools for your project?
  • Does that team have prior experience using the tool(s) you are considering?
  • If not, do you have detailed documentation and a proven training approach to bring the workforce up to speed?
  • Do you have a process by which you can ensure the required level of quality for your project?

4) Do you need a vendor or a partner?

The company you buy a data annotation tool from can be just as important as the tool itself. Here, you’ll want to consider how easy it is to do business with the company that’s providing the tool and their openness for collaboration. AI development is an iterative process, and you will need to make changes along the way. Are they willing to consider feedback or ideas for new features for their tool that would make your tasks easier or make your AI models run cleaner and with better results? Aim to find a partner who is willing to work with you on such things, not simply a vendor to provide a tool.

As you research your workforce options, you may discover some data labeling services that provide their own tool. However, be careful not to tie your tool to your workforce unnecessarily. You’ll want the flexibility to change either your workforce or your tool, based on your business needs and the solutions available to you, especially as new tools and workforce options emerge. A data labeling service should be able to provide best practices and share recommendations for choosing your tool based on their workforce strategy.

Also, keep in mind that your annotation tasks are likely to change over time. Every machine learning modeling task is different. The set of instructions you are using to collect, clean, and annotate your data today may change in the coming weeks - even days. Anticipating those changes is helpful, and you’ll want to consider that when you’re making the decision about the data annotation tool you select and the workforce that will use it to label your data.

Here’s a closer look at some of the data annotation tools we consider to be among the best available on the market today.

Commercial Data Annotation Tools

Commercially-viable data annotation tools are likely your best choice, particularly if your company is at the growth or enterprise stage. If you are operating at scale and want to sustain that growth over time, you can get commercially-available tools and customize them with few development resources of your own.

Open Source Data Annotation Tools

Open source data annotation tools allow you to use or modify the source code. You can change or customize features to fit your needs. Developers who use open source tools are part of a collaborative community of users who can share use cases, best practices, and feature improvements made by altering the original source code.

Open source tools can give you more control over features and can provide great flexibility as your tasks and data operations evolve. However, using open source tools comes with the same commitment as building your own tool. You will have to make investments to maintain the platform over time, which can be costly.

While open source tools can be good for learning or testing early versions of a commercial application, they often present barriers to scale. This is because most open source tools are not comprehensive labeling solutions and lack robust dataset management, label automation, or other features that drive efficiency (like data clustering). In addition, few open source tools provide quality assurance workflows or accuracy analytics which can hinder data quality.

It’s important to know that open source communities provide support mostly via on-line documentation, FAQs, and tutorials. There are no support numbers to call and some open source tools don’t provide data privacy and security measures needed to comply with GDPR and HIPAA.

There are several open source data annotation tools available, many of which have been available for years and have improved over time.

You will uncover buy vs. build implications throughout your product development lifecycle. From sourcing the data to labeling, modeling, deployment, and improvements - your data annotation tool plays a key role in your project’s success. That’s why your tool choice is so important - because it affects your workflow from the beginning stages of model development through model testing and into production.

With a market size of USD $805.6 million in 2022 , data annotation tools will expand as adoption of data annotation tools increases in the automotive, retail, and healthcare industries. As new options emerge, you may want to consider what is available to you.

Why change data annotation tools?

As you train, test, and validate your model - and even as you tune it in production, your data annotation needs may change. A tool that was built for your first purpose might not serve you as well in the future as your use case, tasks, and business rules evolve. That’s why it’s important to avoid getting into a long-term contract with a single tool or workforce provider - or tying your tool to your workforce.

Here are a few examples of reasons you might want to change your tool during a project:

  • You began building a tool but are now considering buying because commercial tools have added new features that meet your needs.
  • The tool doesn’t have the automation or the automation features you want.
  • Your cost increases for access to the commercial tool.

How do I change data annotation tools?

When you change your data annotation tool in the middle of training or production, you’ll likely ask the same questions you’d ask if you were buying the tool for a new project. However, there will be considerations regarding the ease of transferring your data into a new tool and resuming data annotation in the new tool.

For example, you will have to anticipate and manage details related to:

  • Introducing a different data ingestion pipeline
  • How data is stored
  • Output format
  • Use of a new tool - and training your data workers to use it
  • Your workforce provider’s technology to track the quality and productivity of its workers, and how they capture the data required to do it.

While we know it’s important to be flexible when it comes to your data annotation tool, we have yet to learn how long one tool can meet your needs and how long you should wait before evaluating your options again. The data annotation tool ecosystem is just gathering steam, and those who were among the first teams to monetize their data annotation tools are just starting to renew contracts with their earliest adopters.

This is one aspect of the market we’re watching so we can provide exceptional consultative service to our clients and ensure they are using the best-fit tool for their needs.

Here are questions to keep in mind when you’re speaking with a data annotation tool provider:

Strategic Approach

  • Of all of the features available with your tool, what does your team consider to be your tool’s specialty - and why?
  • How long have you been building, maintaining, and supporting this data annotation tool?
  • How is your tool different from other commercially-available tools?
  • Do you consider your product to be a tool or a platform? What other aspects of the machine learning data labeling process does your tool support?
  • Is your team open to receiving feedback about your data annotation tool, its features, and ways it could be improved to better serve the needs of our use case?
  • What are your pricing methods? (e.g., monthly, annual, by annotation, by worker)

Key Features

  • Do you offer dataset management?
  • Where can files be stored? What capacity does the tool support, in terms of how much data can be moved into the tool? Can I upload pre-annotated images into the tool?
  • Do you offer an API and/or SDK? If so, how robust are they?
  • Do you offer data management?
  • Can I bulk upload classes and attributes into the tool?
  • Does your tool allow us to deploy a large and growing workforce to use it?
  • What security compliance or certifications does your tool have?
  • Is quality control (QC) built into your tooling platform? What does that workflow look like?
  • What kind of quality assurance (QA) do you provide?

Machine Learning

  • Have you built any AI into your tool?
  • Can I bring my own algorithm and plug it into your tool?

Though the specific tools suggested above are a great place to start, it’s best to avoid dependence on any single platform for your data annotation needs. After all, no two datasets present exactly the same challenges, and no particular tool will be the best option in all circumstances. Because training data challenges are unique and dynamic in nature, tying your workforce to one tool can be a strategic liability.

For a more flexible approach to labeling text, images, and video, you’ll need to develop a versatile team that can adapt to new tools. At CloudFactory, this emphasis on versatility guides how we select and train our cloud workers. We hire team members with the skills to work on any platform our clients prefer. No matter the tool you use or the type of training data you need, we have workers ready and able to get started.

The People + Process Component

The maturity of your data annotation tool and its features impact how you and your data workforce will design workflow, quality control, and many other aspects of your data work. A tool that doesn’t take your workforce and your processes into consideration will cost you time and efficiency in building workarounds for things that you’ll wish were native within the tool.

CloudFactory delivers the people and the process, and we know data annotation because we’ve been doing it for the better part of a decade, working remotely for our clients. Our data annotation teams are vetted, trained, and actively managed to deliver higher engagement, accountability, and quality.

  • Work from anywhere - We work how you work, as an extension of your team. We can use any tool and follow the rules you set. Using our proprietary platform, you have direct communication with a team leader to provide feedback. Workers can share their observations to drive improved processes, higher productivity, and better quality.
  • Scale the work - We can flex up or down, based on your business requirements.
  • Select and train top-notch workers - Our workforce strategy values people, and we make sure workers understand the importance of the tasks they are doing for your business. We monitor worker performance for productivity and quality, and our team leaders come alongside workers to train and encourage them.
  • Flexible pricing model - You can scale work up or down without renegotiating your contract. We do not lock you into a long-term contract or tie our workforce to your tool.

Are you ready to select the right data annotation tool? Find out how we can help you save time and money.

Reviewers Anthony Scalabrino , sales engineer at CloudFactory , a provider of professionally managed teams for data annotation for machine learning.

Nir Buschi , Co-founder & Chief Business Officer at Dataloop AI , an enterprise-grade data platform for AI systems in development and in production, providing an end-to-end data workflow including data annotation, quality control, data management, automation pipelines and autoML.

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Frequently asked questions, what is annotated data.

In supervised or semi-supervised machine learning, annotated data is labeled, tagged, or processed for the features you want your machine learning system to learn to recognize. An example of annotated data is sensor data from an autonomous vehicle, where the data has been enriched to show exactly where there are pedestrians and other vehicles.

What is a data annotator?

A data annotator is: 1) someone who works with data and enriches it for use with machine learning; or 2) an auto labeling feature, or automation, that is built into a data annotation tool to enrich data. That automation is powered by machine learning that makes predictions about your annotations based on the training data it has consumed and the tuning of the model during testing and validation.

What is data annotation?

In supervised or semi-supervised machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict. You are enriching - also known as labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize. Ideally, once you deploy your model, the machine will be able to recognize those features on its own and make a decision or take some action as a result.

What are data annotation tools?

Data annotation tools are cloud-based, on-premise, or containerized software solutions that can be used to label or annotate production-grade training data for machine learning. They can be available via open source or freeware, or they may be offered commercially, for lease. Data annotation tools are designed to be used with specific types of data, such as image, text, audio, spreadsheet, sensor, photogrammetry, or point-cloud data.

What is an image annotation tool?

An image annotation tool is a cloud-based, on-premise or containerized software solution that can be used to label, tag, or annotate images or frame-by-frame video for production-grade training data for machine learning. Features may include bounding boxes, polygons, 2-D and 3-D points, or segmentation (semantic or instance), or transcription. Some image annotation tools include quality control features such as intersection over union (IoU), a consensus model used in object detection within images. It compares your hand-annotated, ground-truth images with the annotations your model predicts.

What’s the best image annotation tool?

The best image annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements. Dataloop , Encord , Hasty , Labelbox , Pix4D , Pointly , and Segments.ai offer commercial annotation tools to label images that are used to train, test, and validate machine learning algorithms. CVAT and QGIS are open source tools you can use and customize for your own image annotation needs.

What is a video annotation tool?

A video annotation tool is a cloud-based, on-premise or containerized software solution that can be used to label or annotate video or frame-by-frame images from video for production-grade training data for machine learning. It can be available via open source or freeware, or it may be offered commercially, for lease. Features may include bounding boxes, polygons, 2-D and 3-D points, or segmentation (semantic or instance).

What’s an online annotation tool?

An online annotation tool is a cloud-based, on-premise, or containerized software solution that can be used to label or annotate production-grade training data for machine learning. It can be available via open source or freeware, or it may be offered commercially. Online annotation tools are designed to be used with specific types of data, such as image, text, video, audio, spreadsheet, or sensor data.

What are text annotation tools?

Text annotation tools are cloud-based, on-premise, or containerized software solutions that can be used to annotate production-grade training data for machine learning. This process also can be called labeling, tagging, transcribing, or processing. Text annotation tools can be available via open source or freeware, or they may be offered commercially.

Is there a list of video annotation tools?

Dataloop , Encord , Hasty , Labelbox , and Segments.ai offer commercial annotation tools that can be used to label video to train, test, and validate machine learning algorithms. CVAT is an open source video annotation tool you can use or customize for your own video annotation needs. The best video annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements.

What’s the best text annotation tool?

The best text annotation tool will depend on your use case, data workforce, size and stage of your organization, and quality requirements. DatasaurAI and Labelbox offer commercial annotation tools that can be used to analyze language and sentiment to train, test, and validate machine learning algorithms.

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list of annotation tools

13 Best Image Annotation Tools of 2023 [Reviewed]

Alberto Rizzoli

Let’s face it—

It's easy to get confused when choosing the best image annotation tool for your specific use case.

Even more so, with a new data training platform entering the market every couple of months and promising innovative features, faster labeling, or higher accuracy. 

But optimizing your data annotation process is crucial to ensure your model's high performance and reliability. So, picking the right tool for your computer vision projects should not be taken lightly.

To help you out, we put together a list of the most popular image annotation tools out there, along with their key features and pricing info.

Here’s what we’ll cover:

SuperAnnotate

Supervise.ly.

Note: Positions 1 through 8 are paid platforms, while 9 through 13 are free image annotation tools.

Speed up labeling data 10x. Use V7 to develop AI faster.

Don't start empty-handed. Explore our repository of 500+ open datasets and test-drive V7's tools.

Feeling short on time or too busy to go through all of the options?

No worries!

We get it :)

That's why we've prepared this handy graphic with Essential Features, Supported Formats , and Security Certificates that allows you to skip the nitty-gritty parts, and easily compare all the tools in seconds.

list of annotation tools

Let's explore each of these more in-depth. First, we’ll have a look at paid image annotations tools.

Let me start by saying that we won’t be outright telling you that V7 is the best image annotation tool out there.

We won’t be promoting ourselves as the “top training data platform” or brag about people naming V7 the most versatile and advanced tool for image and video annotation .

None of that.

Our own article isn’t the right place to make such bold statements.

We’d like to encourage you to try out V7 for yourself and see whether we deserve all the positive reviews we’ve got ;-)

V7 is an automated annotation platform combining dataset management , image annotation , Video annotation , and autoML model training to automatically complete labeling tasks.

V7 enables teams to store, manage, annotate, and automate their data annotation workflows in:

- DICOM medical data

- Microscopy images

- PDF and document processing

- 3D volumetric data

Key features include:

  • Automated annotation features without prior training needed
  • Composable workflows allowing multiple models and human in the loop stages
  • Dataset management that stays robust at large scale
  • Integrated data labeling services
  • Real-time collaboration and fluid UX
  • Frame-perfect Video annotation tool

Price: From USD 0 (Education Plan), more details on the V7 pricing page

💡 Read more: 9 Essential Features for a Bounding Box Annotation Tool.

Advantages:

  • Automation features can be used by non-technical users
  • Composable workflows allow solving complex, multi-stage tasks
  • Suited for medical image annotations (FDA, CE Compliant, and HIPAA compliant)
  • Supports most unique file types (ultra-high-resolution, multi-spectral, microscopy formats, PDF)

Labelbox is a training data platform built from three core layers that facilitate the entire process from labeling and collaboration to iteration. It was created in 2018 and has quickly become one of the most popular data labeling tools.

list of annotation tools

Labelbox offers AI-enabled labeling tools, labeling automation, human workforce, data management, a powerful API for integration, and a Python SDK for extensibility.

It enables annotations with polygons, bounding boxes, lines, as well as more advanced labeling tools.

Key features:

  • AI-assisted labeling (BYO models)
  • QA/QC tooling and label review workflows
  • Strong labeler performance analytics
  • Customizable interface to simplify tasks

Price: Free 5000 images/Custom Pro and Enterprise plans.

  • Superpixel coloring option for or semantic segmentation
  • UX friendly interface
  • Advanced Performance and quality control monitoring
  • Enterprise-friendly plans and SOC2 compliance

💡 Pro tip: Check out Labelbox vs. V7 to learn more.

Scale is a data platform that enables annotations of large volumes of 3D sensor, image, and video data.

.

Scale offers ML-powered pre-labeling, an automated quality assurance system, dataset management, document processing , and AI-assisted data annotation eschewed towards data processing for autonomous driving.

This data annotation tool can be used for a variety of computer vision tasks, including object detection , classification, and text recognition and it supports multiple data formats.

Key Features:

  • ML-powered pre-labeling
  • Nucleus dataset management
  • Automated QA system with gold sets
  • Document processing features
  • Model-in-the-loop data curation

Here you can learn more about Scale AI alternatives.

Price: From $50,000.

  • Synthetic data generation
  • Super-pixel segmentation
  • Strong for autonomous driving use cases involving LIDAR and mapping

💡 Pro tip: Check out Scale vs. V7 to learn more.

Superannotate is an end-to-end image and Video annotation platform that streamlines and automates computer vision workflows.

list of annotation tools

SuperAnnotate allows you to create high-quality training datasets for various computer vision tasks, including object detection, instance, and semantic segmentation, keypoint annotation, cuboid annotation, and video tracking. 

Available tools include vector annotations (boxes, polygons, lines, ellipses, keypoints, and cuboids) and pixel-wise annotation using a brush.

  • AI-assisted labeling (BYO Models)
  • Superpixels for semantic segmentation
  • Advanced quality control systems
  • Supports various formats through image conversion

Price: Free 14-day trial / Custom Starter, Pro, Enterprise plans

  • Offers free web-based tool created in cooperation with Open CV
  • Advanced project management features (analytics, filtering etc.)

💡 Pro tip: Check out SuperAnnotate vs. V7 to learn more.

The all-in-one cloud-based annotation platform with embedded tools and automation for producing high-quality datasets.

list of annotation tools

Dataloop accommodates the whole AI lifecycle including annotation, model evaluation, and model improvement by utilizing the human feedback in the loop.

It offers tools for basic computer vision tasks like detection, classification, key points, and segmentation. Dataloop supports both image and video data.

  • Model-assisted labeling
  • Multiple data type support
  • Advanced team workflows with streamlined data indexing and querying system
  • Video support

Price: Free trial / Custom Enterprise plans

  • Automation & production pipeline using Python SDK and Rest API

💡 Pro tip: Check out Dataloop vs. V7 to learn more.

Playment is a fully managed data labeling platform generating training data for computer vision models founded in 2015.

list of annotation tools

Playment supports image and video data, and offers a wide range of basic annotation tools including bounding boxes, cuboids, polygons or landmarks.

It works on the principle of microwork, breaking down large problems into micro-tasks and distributing them among its large community of trained annotators.

  • Fully managed—it only requires that businesses share data and label guidelines
  • Allows for attributes extraction
  • Document management (human-assisted OCR)
  • Product comparison and competitor analysis features
  • Build-in advanced quality control tools 
  • Strong for autonomous driving teams

Supervise.ly is a web-based image and Video annotation platform where individual researchers and large teams can annotate and experiment with datasets and neural networks . 

list of annotation tools

Apart from basic annotation tools such as boxes, lines, dots, polygons, or bitmap brush, Supervise.ly offers a Data Transformation Language tool and enables 3D Point Cloud.

  • AI-assisted labeling
  • Multi-format data annotation & management
  • Option do develop and import plugins for custom data formats
  • 3D Point Cloud
  • Options for project management on different levels for teams, workspaces, and datasets. 

Price: Free 100 images in community edition.

  • Comes with Supervisely Agent—a simple open-source task manager available as a Docker image.
  • Option to draw holes within the polygons
  • Data Transformation Language tools

💡 Pro tip: Check out Supervisely vs. V7 to learn more.

Hive Data is a fully managed data annotation solution to source and label training data for AI / ML Models.

list of annotation tools

Hive Data supports image, video, text, 3D Point Cloud annotation and data sourcing. Apart from basic annotation types, Hive Data offers multi-frame object tracking, contours, and 3D panoptic segmentation.

  • Available data sourcing
  • Fully managed, end-to-end data labeling services
  • Offers pre-trained models
  • Advanced project management workflows

Now, let’s have a look at several open-source, free image and video annotation tools that you can use for your computer vision and data science projects.

CVAT (Computer Vision Annotation Tool) is an open-source, web-based image and video annotation tool for labeling data for computer vision, supported and maintained by Intel.

list of annotation tools

CVAT supports the primary tasks of supervised machine learning: object detection, image classification , and image segmentation. It offers four basic types of annotation: boxes, polygons, polylines, and points.

  • Semi-automatic annotation
  • Interpolation of shapes between keyframes
  • Dashboard with a list of annotation projects and tasks
  • Supports a large number of automation instruments including automatic annotation using the TensorFlow* Object Detection API or video interpolation.

Price: Free

  • Web-based and collaborative.
  • Easy to deploy—CVAT can be installed in the local network using Docker, but must be maintained as it scales.
  • Semi-automatic annotation.

💡 Pro tip: Check out The Complete Guide to CVAT—Pros & Cons

LabelMe is an online annotation tool created by the MIT Computer Science and Artificial Intelligence Laboratory. It provides a dataset of digital images with annotations.

The dataset is free and open to external contributions.

💡 Pro tip: Check out Labeling with LabelMe: Step-by-step Guide [Alternatives + Datasets]

list of annotation tools

Labelme supports six different annotation types such as polygon, rectangle, circle, line, point, and line strip. One of the limitations is that files can only be saved and exported in JSON format.

  • Control points modification
  • Segments and polygons removal
  • Six types of annotations

A graphical image annotation tool to label objects using bounding boxes in images. It is written in Python. You can export your annotations as XML files in PASCAL VOC format.

list of annotation tools

In its default version, Labelimg only offers one annotation type—a bounding box or rectangle shape. However, one can also add another shape with a code using a GitHub page. 

  • Annotations are saved as XML files in PASCAL VOC
  • Needs to be installed locally
  • Only image annotation

VoTT (Visual Object Tagging Tool) is a free and open-source image annotation and labeling tool developed by Microsoft.

list of annotation tools

VoTT provides end-to-end support for generating datasets and validating object detection models from video and image assets.

  • Option to tag and annotate Image Directories or Stand alone videos
  • Tags and assets export to CNTK, Tensorflow (PascalVOC), or YOLO format
  • Offers an extensible model for importing/exporting data from local and cloud storage providers
  • Enables running and validating a trained CNTK object detection model on new videos to generate stronger models

ImgLab is an open-source and web-based image annotation tool.

list of annotation tools

ImgLab provides multiple label types such as points, circles, boundary boxes, polygons. It also supports various formats including dlib, XML, Pascal VOC and COCO .

  • Web-based and local versions
  • Basic IDE features
  • Supports multiple label types and file formats

How to choose the best image annotation tool

There you have it—the list of 13 best free and paid image Annotation Tools. 

Whether you are labeling small sets of data or building complex AI models, picking the right tool for your specific use case will directly impact both—the annotation process and your results. 

Make sure to carefully consider all of your options to avoid wasting your time and money on a tool that doesn’t cater to all of your needs.

Finally, here's a visual comparison of all paid tools we've reviewed in this article.

Best image annotation tool comparison

And if you still cannot decide on the best image annotation solution—send us an email or book a call with our sales team to discuss your options.

💡 Read more:

Annotating With Bounding Boxes: Quality Best Practices

YOLO: Real-Time Object Detection Explained

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6 AI Applications Shaping the Future of Retail

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6 Innovative Artificial Intelligence Applications in Dentistry

7 Job-ready AI Applications in Construction

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Mockup of model training in V7

Previously CEO at Aipoly - First smartphone engine for convolutional neural networks. Management & Stats grad at Cass Business School and Singularity University. Never had a real job.

“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”

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list of annotation tools

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Try out CELUM Content Collaboration

Blog » Online Proofing » 13 Best Annotation Tools to Try

13 Best Annotation Tools to Try

  • Sara Dimovska
  • August 23, 2023

list of annotation tools

Are you looking to markup files and documents? Wondering what are the best annotation tools to use?

Table of Contents

FACT: Collaborating on content gets so messy sometimes. Great ideas get lost in endless email chains. Feedback and comments often lack context, making them impossible to act on. And even when you’re trying to explain them better, there’s still something missing. 

But it all changes when you can add comments directly on the piece of content. When you can annotate an image or video, leave comments beside a paragraph of text, mark elements on web pages that need changing, etc.

Enter annotation tools, software that allows content creators to do just that. 

In this guide, you’ll discover 13 of the best annotation tools today and learn how they can improve your feedback and approval workflows . 

So, let’s get to it. 

What Exactly is Annotation Software?

The term – annotation software – refers to tools that allow you to mark and draw on other files – images, videos, text files, PDFs, and more – to provide precise comments and instructions. 

Different annotation tools use different types of annotation, from simple text comments and notes to allowing users to color-code and label elements of a document, add signatures, and more. 

list of annotation tools

When to use the annotation software?

Organizations use annotation tools in a variety of situations. Here are just some examples:

  • Many companies implement annotation software to comment and provide contextual feedback on content assets across the entire content production process. Such platforms help team members provide more meaningful feedback that can be acted on quickly. 
  • Annotation tools help teachers highlight important passages in text files submitted by students. 
  • Many organizations use annotations to add suggestions for improvements to various assets they work on and data and other file formats. 
  • Teachers and educational institutions often use collaborative annotation to help students develop their reading, writing, and critical thinking skills.

Overall, annotation tools are ideal when you need to provide meaningful feedback and leave comments directly on a file or a data set. 

But there’s a problem, right? Search for annotation tools in Google, and you’re immediately overwhelmed by the number of options available. 

How do you decide which software to use?

Here are some suggestions for how to evaluate the tool. And further down in this guide, you can review 13 of the best annotation tools today to evaluate for your annotation process.

What to Consider When Selecting an Annotation Tool

Most annotation tools focus on specific uses or use cases. Some help with annotating files. For others, annotation features are only part of a larger project management platform that helps companies deliver content more efficiently. 

For that reason alone, you should evaluate each tool against these three criteria:

#1. Functionality

Consider what you will be using the tool for. Do you need it just to annotate files? Or does your team struggle with creating content efficiently? Some annotation tools will help you improve feedback; others will also provide features to scale content production. 

Each of the tools on our list has its distinct set of features. So, as the first thing, check whether the software’s key features match what you’ll be using it for. 

#2. Annotation capabilities

Secondly, different tools focus on different file formats. Most allow marking up text or PDFs. Many offer image annotation options, and some also allow you to annotate videos or databases. 

When evaluating annotation tools, also pay attention to what file types the tool works with. 

#3. Efficiency

Annotating files seems simple to do. But from the software point of view, it’s a complex process that requires adding more information to an existing file. Some tools handle this better than others. They work slower on certain file types and may even struggle to process more complex assets. 

So, if you find tools you like, take them for a trial. See how well the tool performs tasks and if you’re happy with the speed and efficiency.

With all that in mind, here are the best annotation tools to consider.

13 Best Annotation Tools for 2023

list of annotation tools

CELUM ( disclaimer: this is our tool ) is one of the most advanced online proofing tools, with equally advanced annotation features. 

Because of that, CELUM offers an incredible range of annotation capabilities:

  • The ability to share precise and actionable feedback directly on files
  • Annotations on videos, images, text, PDFs, and other common file formats. 
  • CELUM gives you access to a complete revisions history to compare different versions of files and monitor how projects evolve. 
  • Mentions to notify other team members about new annotations.
  • Email notifications and triggers to keep everyone informed, and more. 

But CELUM offers a whole range of other capabilities to help your team create content faster and at a scale. 

  • Online proofing that makes review and approval processes a breeze.
  • Content hub to keep all assets in one place, easy for everyone to find.
  • Project management with advanced Kanban boards, tasks, and project templates. 
  • Content workflows will help you streamline creative processes.
  • Finally, a whole range of integrations will make CELUM an amazing part of your creative stack.

Pricing: CELUM offers a free plan for small teams of up to 10 people. Bigger teams can avail of paid plans with additional functionality starting at €14.90/per month. 

Looking for an annotation tool?

Want to see how CELUM can help your brand improve annotations and scale content production?

#2. Filestage

list of annotation tools

Filestage is another online proofing tool that offers annotation tools as part of its toolstack. With Filestage, teams can add feedback on almost all common file formats and add annotations in context. 

However, Filestage’s main focus is on centralizing review and approval processes and allowing teams to save time and just get on with their creative work. 

Pricing: Filestage pricing starts at €12 per user per month. Team plans start at €24 per month per seat.

#3. Annotate

list of annotation tools

Annotate is a document collaboration tool that helps teams take their documents from the first draft to a completed asset. Annotate focuses on allowing teams to create documents on time, and for that reason, the tool focuses on selected file formats only – text files, PDFs, etc. 

One of the unique features of Annotate is the ability to store documents on a private cloud or even on your servers. 

Pricing: Annotate offers a limited free plan. The company doesn’t disclose paid plans on the site, and you need to contact them directly for those. 

#4. Doccano

list of annotation tools

Doccano is open-source software that incorporates machine learning to expand traditional annotation features. With Doccano, you can perform sentiment analysis of your text, text classification, use data annotation tools, and even use sequence to sequence learning to create training data models. 

Pricing: Doccano is a free tool.

list of annotation tools

Unlike other apps on our list, Ink2Go is desktop software. This means that you need to install it on your computer. But it also means that you can use it on top of almost any other software you’re using, even on video. Ink2Go then lets you save those annotations as images to share further. 

Pricing: Ink2Go requires a one-time license of $19.90.

#6. PDF Annotator

list of annotation tools

As the name suggests, this annotation tool works with PDFs only. But it offers a whole range of features to annotate this file format. You can comment on any PDF, edit it, add corrections and highlights, and even add photos and drawings. You can then save your annotations in the original file to share them with your team and restore the original document without comments easily. 

Pricing: PDF Annotator is also a desktop tool, and a single license costs €69.95. 

#7. Markup Hero

list of annotation tools

MarkupHero is a screenshot and annotation tool that aims to speed up your content creation workflow. With MarkupHero, you can communicate with the team better through the screenshot app and image and PDF uploader. You can also annotate an image, PDF, or website using various annotation features. 

Pricing: MarkupHero offers a free plan. It also offers a paid plan at $4/mo per user.

#8. Zoho Annotator

list of annotation tools

Zoho Annotator is a screen capture and annotation browser plugin that helps to add contextual comments on web pages without leaving your browser. It’s an ideal tool for teams that regularly review website designs and need to provide comments and instructions to other stakeholders. 

With Zoho Annotator, you start by taking a snapshot of a website. Then, you use annotation tools to provide feedback, highlight potential errors or mistakes, etc. You can also blur or smudge confidential data before sharing the image with your comments.

Pricing: Zoho Annotator is a free Chrome extension.

#9. ClickUp

list of annotation tools

ClickUp is a robust project management platform bringing together various capabilities and tools to allow teams to do all of their work in one place. With ClickUp, you can plan projects, collaborate on documents, chat and exchange ideas, and more. 

But ClickUp also features an annotation tool that allows you to markup PDFs and images directly from the tool. 

Pricing: ClickUp offers a free plan. Paid plans start at $9 per month per user.

#10. GoVisually

list of annotation tools

GoVisually is an approval and review software helping companies move away from offering feedback on content via email. Instead, the company offers a whole range of proofing solutions to provide visual feedback and annotations directly on design files, images, PDFs and video content.

With GoVisually, you can upload your files to the platform, add feedback and mark them up before sharing them further with other stakeholders. 

Pricing: GoVisually offers two pricing plans. The first one costs $25 per month and is ideal for freelancers. The Team plan starts at $60 a month for 5 users. 

list of annotation tools

Diigo is another Chrome extension that allows you to annotate and markup online PDFs and webpages directly from the browser. With Diigo, you can add sticky notes, highlight content sections, place tags, outline documents, and more. 

On top of that, Diigo lets you build a list of bookmarks, tag your collected web pages, and share your research with colleagues. 

Pricing: Diigo offers a limited free plan. Paid plans start at $40 per year. 

#12. Superannotate

list of annotation tools

Superannotate is quite a unique annotation tool on our list. It does provide a whole range of capabilities to annotate data. However, its focus is on annotating and managing ground truth for artificial intelligence. 

With SuperAnnotate, you can create high-quality training datasets for object detection, instance, semantic segmentation, keypoint annotation, data labeling, or video tracking. 

Pricing: SuperAnnotate does not make its pricing public.

#13. RedPen

list of annotation tools

RedPen is all about making content collaboration as easy and fast as possible. The tool allows you to upload designs, add comments and annotations, and ask colleagues for feedback. You can also organize your work into projects to ensure that all assets are in one place and keep track of each file revision. 

Pricing: RedPen offers pricing plans based on the number of projects. Plans start at $20/mo for 5 projects.

And there you have it…

Thirteen of the best annotation tools to capture feedback in context, and speed up team collaboration.

All that’s left for you is to review each of them and pick the one that suits your needs the most. 

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25 Best Annotation Software Tools (Free & Paid Apps)

In this post, we will dive into the 25 best annotation software tools that you can use for your specific personal and business needs.

💡 KEY INSIGHTS

  • Flexibility in annotation tools is crucial for adapting to various data types and annotation needs.
  • Advanced annotation tools significantly reduce the time required for data labeling through automation features.
  • Ensuring data privacy and security is a paramount consideration when choosing annotation tools for sensitive projects.
  • After evaluating numerous options, we selected the top 25 annotation tools, with PDF Reader Pro ranking as #1 for its comprehensive features.
Each tool comes with its pros and cons and there is a tool suited for every need.

There are advanced online annotation tools that exist to make our lives easier , from editing notes to summarizing important information, keeping track of to-do lists, all the way to editing images and video.

We use these tools every single day and all of which have their own unique features and can help us accomplish our everyday tasks with ease.

So, What Are Annotation Tools?

3 things to consider when choosing an annotation tool, 25 best annotation tools (free and paid).

Important disclosure: we're proud affiliates of some tools mentioned in this guide. If you click an affiliate link and subsequently make a purchase, we will earn a small commission at no additional cost to you (you pay nothing extra).

Simply put, they are text or drawing tools that help us add information to text, video, or image documents and various content pieces.

They can also be used in many different types of databases, such as Scale and Doccano.

There are countless tools out there and our job is to make the search easier for you.

Each tool has its particular uses , depending on your annotation goals, there will be different options for every need.

👉🏼 There are countless paid and free apps that are available on the market today.

One of the first things to consider when deciding which is best for you is the price (or perhaps no price at all).

Whether your choice is free or paid, consider the features that come with both versions, you may be getting a great deal with the paid versions that allow you to use all the features within the app at a low cost.

On the flip side, a free tool may be best if you are using the basic features on the app and see no reason to pay for extra features. It all depends on what you plan to use the app for.

2. The Functionality

This is probably one of the most important points to consider.

Ask yourself

  • What will I be doing with this tool?
  • Why am I using this tool?
  • What can this tool do for me to make my life easier?
To make this decision simple, the tool should have the functionality you are looking for.
  • Will you be using the tool for studies and taking down notes ?
  • Will you be editing documents on a large scale and need fast and accurate software that saves you time?

Each tool possesses its own set of functional features and suits every person's specific needs.

3. Quality And Efficiency

This point all comes down to how well does the app run and how well it performs the tasks it needs to.

Many free tools may not always detect every error that needs correcting or a phrase that can be worded differently.

This is usually a consequence of insufficient QA (Quality Assurance) from the software itself.

👉🏼 Paid versions of some tools provide a more trustworthy base for picking up on errors .

However, the price may not always be worth the extra efficiency that the tool provides and there are still some effective free tools on the market, just with fewer features.

Now that you have a foundation to build upon, here are 25 tools that have made the list, both free and paid versions.

We will dive into each one's pros and cons so that you can make the best decision for your annotation wants and needs.

  • PDF Reader Pro
  • Adobe Acrobat Pro DC
  • Markup Hero
  • PDF Annotator
  • Screenlight
  • Zoho Annotator

1. PDF Reader Pro

PDF Reader Pro stands out as a remarkable annotation tool, distinguished by its user-friendly interface and comprehensive set of features. From our first-hand experience using this tool, we found it particularly efficient for a variety of tasks.

One of the key strengths of PDF Reader Pro is its versatility in handling different annotation types.

Whether it's highlighting text, adding notes, or even drawing directly on the document, the tool offers a seamless experience. The annotation process is intuitive, making it easy for users of all skill levels to navigate and utilize the tool effectively.

Here's More About Our Experience Using PDF Reader Pro

During our exploration of annotation tools, we had the chance to use PDF Reader Pro extensively, and our experience was exceptionally positive. It proved to be a highly effective tool for managing and annotating PDF documents.

Key Aspects Of Our Experience:

  • Intuitive Annotation Features: PDF Reader Pro impressed us with its wide range of annotation capabilities. The ease of highlighting, adding notes, and drawing on documents made our annotation tasks significantly more efficient.
  • Seamless Document Navigation: Managing and navigating through annotations was straightforward. This was particularly useful when working with large documents, as it allowed us to easily track and revise our annotations.
  • Multi-Platform Compatibility: We appreciated the tool's compatibility across different devices, which facilitated a seamless workflow whether we were on desktops or mobile devices.
  • Robust Security Features: The security features, including encryption and password protection, provided peace of mind when handling sensitive documents.

Overall Impression: Our experience with PDF Reader Pro was highly satisfactory. The tool stood out for its user-friendly interface, comprehensive annotation features, and robust security measures.

Our Rating: Based on our hands-on experience, we rate PDF Reader Pro a solid 4.8 out of 5 stars.

We weren't the only ones that have benefitted from using PDF Reader Pro. While doing our research we found reviews showcasing others' experiences.

Pros And Cons

PDF Reader Pro has a free trial available.

After the trial period, you can subscribe for a once-off price of $79.99/lifetime.

2. Adobe Acrobat Pro DC

Image Source: Adobe Acrobat

With Adobe Acrobat Pro DC you can access PDF documents and comment, edit, share, add virtual sticky notes and send files amongst friends and colleagues with ease.

Adobe Acrobat Pro has many features which allow for the sharing of PDF files, converting, combining, and editing documents all in one place. You can convert PDF documents to:

  • PowerPoint (PPT)
  • Images to PDF format

There are unlimited projects to work on within the software and you can be sure to have all your work under one roof, making it that much easier and convenient.

list of annotation tools

This tool is not free and requires a monthly subscription of $14.99.

While this can work out to be a bit expensive annually, the features that come with Adobe Acrobat Pro DC and its high quality make the purchase more worthwhile.

  • Many PDF conversion options
  • Efficient editing features
  • Easy to navigate
  • Effective sharing capabilities
  • Available on mobile
  • Easy participation
  • Simple PDF management
  • Frequent updates
  • The editing tab is quite clunky

Adobe Acrobat Pro DC is supported on Windows, Mac, and Mobile.

list of annotation tools

Markup is one of Kdan Mobile's apps that provide basic tools that function at the highest level of productivity.

Markup is perfect for business professionals, but also for students who need to edit and summarize work at the click of a finger. From PDF files to website content, you can work on almost anything.

With Markup, you can collaborate with your entire team and make document review simple. Markup has an AI Auto Summary feature that allows all your work to be summarized quickly.

Summaries can be saved on your device for when you want and need to review workflow and make some changes.

Markup is a free-to-use tool. There are, however, Inn-App purchases that can be bought to add extra features to the app, should you need them.

  • Easy to use
  • Sufficient features with the free version
  • AI Auto Summary feature
  • Convenient collaboration
  • The browser extension can be buggy
  • Mobile version occasionally slow

Markup is available as a web extension on both Windows and Mac, it is only available on iOS Mobile devices and tablets.

list of annotation tools

With ClickUp, you can expect to create informative internal and external documents, edit existing docs and create wikis and impressionable knowledge bases.

ClickUp allows you to edit and create most files , from PDF documents all the way to Google Docs.

ClickUp boasts many features, but some of its key features include:

  • Commenting on documents
  • Notes tools
  • Syncing calendars
  • Arranging and organizing multiple priorities, and formulating checklists

There are over 100 exclusive features within this annotation app.

ClickUp allows for numerous app integration, including Slack, Google Drive, Dropbox, Microsoft Teams, and Zoom, just to name a few.

It is an advantage to have an annotation app that possesses screen-sharing options like those found in Zoom and Microsoft Teams.

For the most part, ClickUp is free to use but can be upgraded to the 'Unlimited' package for only $5 per month when paid on an annual basis or $9 per month when paid monthly.

  • Over 100 features
  • Numerous app integration
  • Displays status of projects
  • AIO (All In One) functionality
  • Connect projects with others
  • Great UI (User Interface)
  • Complex learning curve
  • App stalls at times
  • Limitations with mobile version

As mentioned above, ClickUp has a web and mobile version. It is available on Windows, Mac, and Mobile, as well as Tablets.

5. Markup Hero

list of annotation tools

Markup Hero is an annotation and screenshot tool with the main objective of improving communication and productivity.

This free tool allows you to take scrolling screenshots of any basic application window or any Chrome tab.

Even though Markup Hero has various features, here are its key features:

  • Screenshots
  • Crop and resize canvas
  • Annotate screenshots , images PDF’s, and URL's
  • Chrome, Google Drive, Slack App, and Annotation API integrations

The Sidekick and Hero plans are both free and enable you to have access to Markup Hero’s basic features.

However, you can decide to upgrade to the Superhero plan for $4 per month (billed annually). This plan was designed for professionals and other teams who need access to all their features without limiting their access.

  • Free version available
  • Annotating PDF’s
  • Simple UI design
  • Edible and accessible from any device
  • Share links to screenshots
  • It is still a newer product

Markup Hero is available on Windows, Mac, and Linux (Beta).

6. NoteLedge

list of annotation tools

NoteLedge is known for its use as a digital notebook that can be used by creatives for inspiration, creative processing, and its multimedia tools make for exciting mood board creation.

Share files and ideas with teammates and clients and create top-notch presentation content too.

NoteLedge allows you to interact with projects like never before and its sketching tools are some of its users' favorite features to use.

NoteLedge has a flexible workspace and lets you arrange your content in however many layers and pages you would like.

  • NoteLedge has three pricing plans.
  • The first plan is free and is only available on iOS devices.
  • The second plan is $2.49 per month and is only available on iOS devices.
  • The third and final plan is $4.99 per month and is available on Android, iOS, Windows, and Mac.
  • Many features with each plan
  • Effective and efficient
  • Ads in the free plan
  • Occasionally crashes

NoteLedge is available on Windows, Mac, Android, and iOS and is dependent on the price plan.

7. zipBoard

list of annotation tools

zipBoard is a web-based annotation and visual feedback tool for almost all types of digital assets . With it, you can review all your online proofs on a centralized platform and get rid of the endless email chains and screen-sharing meetings.

zipBoard lets you annotate images, videos, webpages (static and live), SCORM, PDFs, etc. and simplifies your visual bug tracking & QA process for reviewers.

zipBoard’s vast array of annotation tools lets you markup directly on top of your screen and share them across the team and stakeholders. It has an easy-to-use, simple UI, especially useful while getting feedback from non-technical clients and stakeholders, which is often the need.

All of these are without creating an account for the guest clients , letting you assign and prioritize the tasks from its dashboard.

With the traceability of all versions and changes and a clear designation of roles, zipBoard helps ensure confusion-free collaboration for all, whether you’re an agency or an individual.

zipBoard comes with 3 different plans and they offer a 15-day free trial in each of them.

  • Team, Agency, Enterprise: Custom ( Contact zipBoard )
  • Unlimited collaborators and projects.
  • The biggest range of annotation tools.
  • Provide your stamp of approval and sign-off creatives.
  • Integrate the product into your ERP. API creation on demand.
  • Best in class sharing features.
  • There is no mobile app.
  • The user community isn’t that big but the active support makes up for it.

zipBoard is entirely web-based.

8. Filestage

list of annotation tools

Filestage is another tool known for its online proofing ( reviewing and approving) of files, including PDFs, videos, images, designs, and audio (making it ideal for speech tool use).

The app's key features include complete commenting tools, sorting files all in one place, and tracking the progress of your files.

With Filestage, one of its powerful features is the automation of file sharing which has allowed for a faster return time of a document's reviews and approvals.

There is no limit to the number of files that can be shared and there is an unlimited reviewer option, send projects to as many people as you like.

Filestage can be integrated with many other apps, with some being Google Drive, Dropbox, Zoom, and Asana.

This makes feedback and approval easy for everyone.

Filestage is not a free-to-use tool, it does have a free trial option, otherwise, it has four pricing options to choose from.

  • The first option is called the 'Essential' and it starts off at $10.66 per month.
  • The second option is called 'Advanced' and starts at $22.50 per month.
  • The third option is called 'Professional' , starting at $46.18 per month.
  • The fourth and final option is known as 'Enterprise' and the amount is determined over email or phone call depending on the business needs.
  • Fast return time on reviews and approvals
  • Unlimited amount of file shares
  • Integration with other useful apps
  • Time-saving
  • Uploading speed can be slow
  • Feature limitations for pro plan

Filestage is available on Windows and Mac.

9. Annotate

list of annotation tools

Annotate allows you to directly draft documents within the app and allows for collaboration throughout the writing process.

A key feature of Annotate is its easy fact-checking system .

You can paste information from another document into Annotate, and it will provide a link to the original source, making it easy to credit references.

Share your documents with people you trust, Annotate allows groups to have discussions about projects and share ideas all within a safe space. You give permission to those you would like to see your files.

Review and edit documents too with ease, and all editing takes place within Annotate , no need to download any files to edit later.

One of the easiest and efficient fact-checking tools out there.

Most of Annotate's functions and features are free to use , however, there are plans for larger teams who need more customizable features. The price for these plans is determined by the number of users and the extra features they require.

  • Organized User Interface
  • File protection
  • Fact-checking system
  • The free version offers a limited amount of file space
  • Can be slow at times

Annotate is available on Windows and Mac and offers basic viewing functions on Mobile.

list of annotation tools

With nTask, it is a select tool for project management . Teams can use this annotation software to plan and analyze important everyday tasks and collaborate with one another on project planning.

nTask offers strategic features that aid in streamlining and managing the resources of projects.

This app also provides useful budgets and other financial summaries to keep track of all the necessary facts and figures.

One of nTasks popular features is their Kanban Boards (project management visualization tool).

Users can visualize projects with Gantt charts (illustrate project schedules) by making use of the Kanban boards and planning their project's process. There are many ready-made board templates to choose from too.

There are four pricing plans that nTask provides.

  • The first plan is free and is recommended for freelancers.
  • The second plan is a ' Premium ' plan and is recommended for SMEs and Startups at $2.99 per month, per user.
  • The third plan is a ' Business ' plan, recommended for larger corporate institutions at $7.99 per month, per user.
  • The fourth and final plan is an ' Enterprise ' plan and the price is determined on demand .
  • Easy to use and collaborate
  • Efficient and effective project management
  • Use of Kanban boards
  • Streamlined deadline view
  • Great integration with other apps (Slack and Zapier)
  • Not enough customization options
  • Attaching documents and images can be tricky

nTask is available on Windows, Mac, and Mobile.

list of annotation tools

Miro is a fast, simple-to-use visual collaboration software that doubles up as a robust documentation and note-keeping tool. At first glance, Miro might seem like just a digital whiteboard — but its capabilities go far beyond that.

It’s designed to let your team collaborate effectively no matter where or when you work. Used by over 45 million people worldwide, Miro has the right tools , tech, and templates to go from idea to execution.

It’s why the platform quickly becomes your central project hub , and a way to always stay connected to your team. You can explore one of Miro’s many ways to communicate — including sticky notes , charts, graphs, and videos.

Miro is designed to fit into your hybrid workflow , and boasts of over 1,000 ready-made templates including mind maps, Kanban boards, Gantt charts, product wireframes , research boards, and flowcharts.

It’s trusted by 99% of the Fortune 100, and used by teams at TransUnion, HP, Upwork, Cisco, Qlik, and Atkins.

  • Free forever plan available
  • Intuitive and easy setup
  • Built-in communication features for streamlined collaboration
  • Visitor/guest accounts locked to paid plans only
  • Zooming can be jumpy on larger projects
  • Free version does not allow high-quality export to PDF

Miro has four pricing options.

  • The free plan is free forever, but is limited to three boards
  • Starter package , priced at $10 per use r per month
  • Business package , priced at $20 per user per month
  • Enterprise package , available at a customized price

Android, Apple, Windows, Mac, Web-app

12. PDF Annotator

list of annotation tools

PDF Annotator is ideal for those who want a select tool that can handle most PDF document purposes . There are many features that can assist with editing, reviewing, and enhancing PDFs.

The PDF Annotator can be used to highlight, add text, images, symbols, signatures, page numbers, and more. All your corrections and additions to the PDF are saved within the software.

You do not have to worry about you or others not being able to see your annotations within the document. You can view your edited PDFs in any PDF software.

PDF Annotator is not free but does have a free trial. It has two main license payment options . The license is operational for a year and is renewed annually.

  • The first license option is ideal for normal business functions and for 1-4 users it is priced at $69.95 per year, excluding VAT.
  • The second license option is perfect for students and university functions and for 1-4 users it is priced at $39.95 per year, excluding VAT.
  • The more users, the cheaper license is annual, with the highest number of users being more than 1000 for $2.47 per year, excluding VAT.
  • Tackles many PDF purposes
  • Performance can be slow
  • Specialized for specific annotation purposes only

PDF Annotator is available on Windows.

13. Marker.io

list of annotation tools

Marker.io allows you to collect feedback and bug reports straight into your favorite issue tracker—without leaving your website. Digital teams and web agencies use Marker.io for internal QA as well as user acceptance testing. With a widget that lives on your website, you can report bugs at the click of a button, saving your team hours in feedback management.

We used to copy/paste client emails. Now, Marker.io automatically creates those issues in Jira.

The best part? This tool automatically captures URL, environment info, console logs, and every other metadata developers might need to reproduce and fix bugs. And because of its deep 2-way integrations with the most popular PM tools (Jira, Trello, Clickup...), you'll never need to spend entire weeks triaging client feedback emails or user bug reports in your inbox again.

Marker.io offers a Starter plan at $39/month, and more features for Team, Company, and Enterprise plans.

  • Easy to use for both your QA team and non-technical stakeholders
  • Deep integrations with over 15 PM tools
  • Intuitive, modern UI
  • All-in-one tool for both internal QA and UAT

Not ideal for mobile apps

Marker.io has both a web and mobile version. It is available on Windows, Mac, Mobile, and Tablets.

14. Wipster

list of annotation tools

Wipster is known for its video proofing tools and is loved by creative teams who seek to review and collaborate with other creatives. Wipster allows you to invite an unlimited number of viewers and reviewers to your projects.

Along with inviting others to view your work, you are allowed to choose how your project is viewed and by whom, with Wipster's effective privacy settings.

Wipster has great integration possibilities, with other annotation software like Adobe, Dropbox, Slack, Wistia, Vimeo, and Brightcove.

Some of Wipster's key features include closed captions, threaded-comments, tracking content, and feedback controls.

Privacy is important and Wipster knows how to make its users feel at ease, knowing their work is safe and protected.

Wipster has three pricing plans.

  • The first plan is free to use for freelancers and the occasional project.
  • The second plan is ideal for teams of small or growing companies at $22.50 per month, per user.
  • The third and final plan is for large companies that have multiple teams and the price is determined based on the specific needs of a company.
  • Strong integrations (especially with Adobe)
  • Organized feedback
  • Fast editing capabilities
  • Safe and secure
  • Can be costly
  • Occasional uploading issues

Wipster is available on Windows, Mac, and Mobile.

15. Dropbox

list of annotation tools

Dropbox is one of the more popular annotation source tools.

One of the main goals of Dropbox is organization and making sure that you can access different file types at anytime Dropbox handles cloud content, traditional files, and multiple web shortcuts.

Dropbox can be used for personal and business-related projects. Its features allow you to store and sync files, share files with others even if they do not have their own Dropbox account, and manage collaborations with other teams or team members securely.

Dropbox helps to make sure all your files can be accessed at any time and ensures that lost files always have a backup, so you never lose out on your progress.

Dropbox is not free and comes with six pricing plans , two for personal use and three for business use, and one free plan for either personal or business use.

  • The first plan is ideal for individuals at $11.99 per month.
  • The second plan is best suited for households at $19.99 per month and allows for six users.
  • The third plan is for solo workers at $19.99 per month.
  • The fourth plan is for growing teams at $15 per user, per month.
  • The fifth and final plan is for complex teams at $25 per user, per month.

The free plan offers limited features and is likely more suited for personal use. For additional features, the paid versions are more appealing.

  • Easy synchronization
  • Data backups
  • Vast storage with paid versions
  • Small storage with the free option
  • The app can be slow at times
  • Costly in the long-run

Dropbox is available on Windows, Mac, and Mobile.

16. Doccano

list of annotation tools

Doccano is a commercial data annotation tool that specializes in-text annotation and functions as a select tool for machine learning practitioners.

Some of its key features include text analysis, sequence labeling, text summarization, and entity recognition (names, locations, and more). Each feature has its own efficient output format.

Doccano also deals with machine translations which helps translate sentences quickly into the desired languages. Doccano lets you annotate your text files in different languages too.

Doccano is a fantastic text tool for web developers and assists in completing projects in a fast and effective manner.

Doccano, however, is also one of the best text tools for users who do not possess a background in coding but need to learn how to manually label the data parallel to those with industry knowledge.

Not many open-source commercial data annotation tools can boast high optimization, but Doccano does, and it shows.

Doccano is an open-source data annotation tool, meaning it is free to use for all.

  • Free to use
  • Supports multiple languages
  • Annotation tools are collaborative
  • Completely manual annotation

Doccano is available on Windows and Mac.

17. Drawboard

list of annotation tools

Drawboard is a feature-rich tool. There are two main tools in the software, Drawboard PDF and Drawboard Projects.

With Drawboard PDF, you have everything you need to edit and review PDF documents with ease and accuracy.

One of Drawboard PDF's appealing features is the drawing tools that claim to feel like you are using real ink and many users love the different pen thickness options and color tools.

The PDF editing tool is simple to use and navigate and has many image resizing tools such as rotating, inserting, cropping, and deleting pages alongside its drawing tools.

With Drawboard Projects , the features are more applicable for team collaborations. Some of the useful features include PDF markups, drawings, and edits.

Drawboard Projects features are helpful to industry professionals like architects and engineers that require details to be added to house plans, drainage systems, and so on, but can be used by any industry professional to share files.

Drawboard has three pricing plans and is not free-to-use software.

  • The first plan is known as 'Basic' and does not have unlimited project access to drawings and templates. It is $20 per month.
  • The second plan is known as 'Standard' and has unlimited project templates for $35 per month.
  • The third and final plan is called 'Enterprise' and the price is determined on the clients' needs.

There are additional features following each plan.

  • Many PDF editing features
  • Fast and efficient
  • Lags at times

Drawboard PDF is available on Windows and Drawboard Projects is available on Windows, iPad, and only iPhone mobiles.

18. Screenlight

list of annotation tools

Screenlight is all about sharing your work with ease. Share files with all the available features that Screenlight offers, such as uploading video, audio speech tools, images, link sharing, reviewing, and complete commenting tools.

Once you have uploaded your files, Screenlight makes sure to develop preview files for you to choose from and enhance your projects. Formatting issues are a thing of the past with this collaborative tool.

Screenlight places an emphasis on its simplicity and time-coded comments that can be entered alongside the video where readers do not have to wait for the comment at a later stage.

It's time-coded comments are a brilliant feature that more sharing tools should offer.

Screenlight has three pricing plans.

  • The first plan is free to use and can only be available to one user.
  • The second plan is known as 'Professional' and is available to 25 users for $9 per user, per month.
  • The third and final plan is known as 'Enterprise' and is available to large companies from $500 per month depending on the needs of the company.
  • Affordable for individuals and small companies
  • Easy collaboration tools
  • Time coded comments
  • Could use more graphic customization
  • Platform availability

Screenlight is available on Windows and Android Mobile.

19. Red Pen

list of annotation tools

Red Pen is a select tool offering fast feedback tools and complete commenting tools.

This app is used for visual collaborating and serves as a place for teams to share files with each other and make use of all the note tools available.

With Red Pen, most of the comments are live and users get to see comments as they happen on the page. The number of collaborators is unlimited, you can invite as many people to review your project as you like.

Red Pen keeps track of all your edited images and welcomes additional versions as you go along.

Red Pen has a free trial option, but the software does have four pricing plans. The price is determined by how many projects you are running at once.

  • The first plan is limited to 5 projects at once at $20 per month.
  • The second plan is limited to 10 projects at $30 per month.
  • The third plan is limited to 25 projects at $60 per month.
  • The fourth and final plan is limited to 40 projects at $90 per month.
  • Free trial option
  • Live commenting tool
  • Occasional bugs
  • Costly in long-run

Red Pen is available on Windows and Mac.

list of annotation tools

Scale is another commercial data tool specializing in audio annotation and speech annotation aimed at NLP (Natural Language Processing).

Scale is integrated with machine learning, all you need to do is send your sensor data to Scale's API (Application Programming Interface) and it will do all the hard work.

Simply put, Scale's main objective is to transform your audio files into text translations that can be used for your specific project needs with its quality speech tools.

Scale is capable of translating different languages and converting those files into easy to handle audio snippets.

Scale can be integrated with Pilot, Google Docs, and Diffgram Data Labelling.

Scale is not a free-to-use software and the price of a plan is determined by your audio output needs. To receive a quote, contact the Scale team for more information.

  • Provides annotations for large volumes of data
  • Great customer service
  • Fast text translating
  • Reliable customer engineers
  • Not supported as an app on Windows or Mac
  • Price is not disclosed on the website

Scale is entirely web-based.

list of annotation tools

Nimbus acts as an image and screencast tool that allows you to send images, videos, screencasts, and screenshots to your friends, family, and colleagues.

With Nimbus, there are many key features available to help you customize your material, like recording, annotating, and making notes on screenshots, images, or video.

Nimbus has a fast upload speed and ensures that whatever you share gets seen quickly.

Nimbus is an innovative screencast tool for teachers, it serves as an effective communicative and collaborative tool amongst teachers, parents, and students.

It recently just developed a new business feature on its platform for helpful clientele collaboration .

There are features available to every users' needs.

Nimbus has three price plans, one of which is completely free.

  • The first option is free and offers a decent amount of helpful features.
  • The second option is called 'Pro' and is priced at $4 per month.
  • The third and final option is called 'Business' and is priced at $6 per month.
  • Enhances productivity
  • Fast upload speed
  • Each plan offers enough features
  • Syncing needs work
  • The mobile app can be clunky

Nimbus is available on Windows, Mac, and Mobile.

list of annotation tools

With Diigo, you can annotate web pages and PDFs while browsing the net at the same time. Diigo allows you to add sticky notes, highlight text, place tags on your favorite web pages, outline your documents and so much more.

Diigo functions well as a multi-tool but especially well as a sharing tool and you can have collaborative discussions with colleagues, classmates, or friends.

You can access your tagged web pages from anywhere and at any time with Diigo's functional archive tool.

Diigo makes use of the Cloud , whenever you are browsing, save your web notes with links and pictures to ensure you never lose track. Diigo aims to make web surfing and editing documents a lot easier.

Diigo offers four pricing plans.

  • The first plan is free but does not come with many features.
  • The second plan is called ' Standard ' and is priced per year at $40.
  • The third plan is called ' Professional ' and is priced per year at $59.
  • The fourth and final plan is called ' Business ' and it is priced per month at $10 , per user.
  • Collaborative options
  • Efficient link saving
  • Limited usage with the free version
  • Can be slow

Diigo is available on Windows, Mac, and Mobile.

23. Zoho Annotator

list of annotation tools

With Zoho Annotator, you can add comments to web pages and save those comments as images . Zoho Annotator works as a screen capturing and screencast tool that allows you to share files on a personal or business level.

You can add to your web snapshots by making use of the drawing tool that the software has to offer, it's all customizable. With Zoho Annotator, you can also smudge out certain words or sentences that you would rather like to be kept private.

You don't just have to use the software for web pages, you can use it to design diagrams, infographics, and mock-ups too.

Zoho Annotator allows for integration with many apps, some of which being Facebook, LinkedIn, Cloud drive, and more.

Zoho Annotator is completely free to use making it an appealing choice.

  • Integrates with many apps
  • Privacy function
  • Cannot screenshot the full page on some websites

Zoho Annotator is available on Windows and Mac as a Google Chrome extension.

24. GoVisually

list of annotation tools

GoVisually is another effective proofing software to help you get the feedback you need or need to give. GoVisually allows people to provide comments with its notes tools, on PDFs, Video, and Design content.

GoVisually is particularly aimed at Designers and Video Creators, Creative Agencies, and Marketing Teams.

Both marketing and design teams can collaborate on projects and provide feedback that is helpful and allows a project to flourish.

With this software everything is seamless and quick , there is no more need for long email chains, this cuts out the middle man.

GoVisually has two pricing plans.

  • The first plan is great for freelancers and is priced at $25 per month.
  • The second plan is suited for agencies and teams and is priced at $60 per month.
  • Practical features
  • Easy uploads
  • Optimizes workflow
  • Unable to upload images in batches

GoVisually is available on Windows, Mac, and Mobile.

25. Hypothesis

list of annotation tools

Hypothesis' Annotation Process focuses on many different groups of interest. These groups include teachers, scientists and scholars, publishers and editors, and journalists.

With Hypothesis, you can embed content into texts, take notes, enrich content, and fact-check various sources.

With teaching, teachers and students can collaborate together on projects using their own annotations and sharing their projects. This allows for thought-provoking insight and conversation in the classroom.

Scholars, scientists, and publishers can benefit from sharing information with one another and adding valuable text to projects with the specific text and speech tools that Hypothesis offers.

Journalists and Editors will find comfort in knowing that the sources they are using have been fact-checked and are suitable for their or your reporting and news writing.

So many people can use this software and find features that work for them and work well.

Hypothesis is free-to-use by anybody making it even more appealing, considering all the features that come with the software.

  • Improves collaboration across the board
  • Customizes content
  • Occasionally slow

Hypothesis is available on Windows, Mac, and Mobile.

Have An Idea Of The Best Tool For You And Your Needs? 💡

The world of annotation software is an overwhelming one, but with this guide, you can be sure to make an informed decision based on everything you or your business needs to function at its best.

There is no right or wrong choice when it comes to these tools, each has its own strengths and drawbacks, you are guaranteed to find the best fit for you.

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The Top 8 Annotation Tools To Do Your Best Work in 2023

  • Hayley Folk
  • September 25, 2023

Imagine you’re writing a technical guide that includes screenshots of an application to help explain its use. After the guide is written, it gets sent to the team to review and edit. And while the guide started as a Google Doc, it was changed into a Word document, and then saved as a PDF. Some employees edited the Google doc, and others commented on the Word doc or the PDF.

To say that editing processes can be a nightmare is an understatement. Trying to connect all edits in one document and make the appropriate changes can blow a project timeline, and the budget, out of the water. Without annotation tools or a process to follow, you will likely find yourself in the Wild West of editing.

Sound familiar? That’s where annotation tools can help. With the correct software, you can seamlessly annotate multiple file types and collect feedback from everyone on your team. Collaborating, annotating, and proofing your content is an important process for many businesses today. Having an annotation tool to manage this process will save time, money, and, most importantly, your sanity. So let’s dive in.

What are annotation tools?

There are many different types of annotations you can make on your digital documents. The most common annotation is text annotation. This type of annotation is when you write directly on top of a document or image to explain or comment on it. Other types of annotations can include:

  • Sticky notes
  • Highlighting
  • Underlining

Each of the annotation tools on our list will have its own set of features, so be sure to explore which ones will work best for your business before making the decision. If you’re not sure which annotation tool is right, ask your team what they need and want from an annotation program.

You may need a tool that is specific to creative design annotations or one that is strictly for editing within a text document. The type of annotations you will be doing will determine the tool you will need. However, once you know what kind you need for your business, you can begin to explore the tools that offer those features.

Our Annotation Software Picks

Creating great content requires a lot of collaboration, feedback, and editing. Completing these tasks without a process to follow or a program to help can lead to many lost resources. With the proper annotation tool, your team can work together on content, making the entire process easier and more efficient.

There are a lot of online annotation tools out there, but we have narrowed it down to the best ones. Read on to learn how these great annotation tools can help your business stay organized and collaborative, regardless of file type.

list of annotation tools

With its proofing and approval functionality , your team will have a faster document turnaround, fewer approval rounds, and the ability to track who approved what. This annotation feature is simple to use and can integrate with a variety of applications, making it a great choice for businesses that need an annotation tool that can do it all. 

Features Include:

  • Approval rounds specific to each user
  • The ability to live-edit of documents
  • URL links for individual proofs
  • Can combine multiple files into one document for proofing

Price: While there is a free version of Hive, the annotation functionality begins on the paid plan , which starts at $12/month. Get started with a free 14-day trial today or learn more about Hive’s customers.

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2. Annotate

annotate app

Annotate is one of the top-of-the-line annotation tools that makes collaborating on documents in real-time a snap. It offers a simple interface that gives its users enough annotation features to be a jackknife for all your text and image editing needs. Built to be a single, secure platform for users to collaborate, Annotate offers a way for cross-organizational teams to work on the same document and save time and resources.

Features include:

  • Freehand notes
  • Native chat provides a space to chat during the review process.
  • Supports custom integrations with many third-party apps
  • Ability to create workflows within documents

Price: Available free for up to 3 users, and then you can upgrade to standard/enterprise options. The pricing for top-tier use is mysterious, and you must contact the sales team for additional information.

3. MarkupHero

Markup Hero

MarkupHero is one of the fastest and easiest annotation tools that works well across a variety of devices. This tool offers real-time proofing within the platform and is perfect for sharing and editing design-related programs. While it can work with documents, it shines with its image markup tool functionality.

With a ton of annotation tools, you can use to communicate your changes, your team can use MarkupHero to save time, stay organized, and always keep on track. Sharing feedback has never been easier with MarkupHero, and it doesn’t hurt that you can also save files in multiple formats!

  • Can export markups as PDFs
  • Markups can be organized within a collection
  • Available on any modern desktop, mobile, or tablet web browser
  • Custom keyboard shortcuts

Price : Available in a free version for a limited number of PDFs. The pricing for additional PDF annotations starts at $4 per month.

4. Drawboard

Drawboard dashboard

Drawboard is one of the top annotation tools that focuses on PDF document annotation. It has many editing features that are perfect for both individual users and teams. With this tool, anyone inside or outside your team can annotate or discuss edits directly on the PDF. Featuring a “layer” management functionality, this tool allows project members to mark up comments in a unique way on multiple levels. An excellent option for design teams, Drawboard offers an efficient way to markup designs quickly and efficiently.

  • Freehand & text annotation
  • Bookmark/searchability within documents
  • Stamps and shape integration

Price: Available in a free version for a limited number of PDFs. The pricing for additional PDF annotations starts at $6.99 per month.

5. SuperAnnotate

superannotate dashboard

SuperAnnotate is an annotation editor that allows you to create high quality labeled datasets with AI-powered features. The software offers flexible editors and powerful automated workflow controls for data management pipelines.

It also has a broad set of analytics tools to help you manage your projects smoothly whether working with a small team or multiple teams across several projects.

With AI-powered features like custom model training and predictions, active learning, and a proprietary tech which can segment images, SuperAnnotate can quicken annotation processes by up to 10x., which makes it one of our top free image annotation tools.

  • Image, video, and text annotation tools
  • Management Infrastructure tools
  • Data Curation and versioning
  • Webhooks and Orchestration

Price: Access to SuperAnnotate is by request only and they do offer a free 1-year plan for up to seven users. There are paid plans available that include more advanced AI-powered features and pricing is provided when you request a demo.

6. Webvizio

webvizio dashboard

Webvizio is a website review and collaboration tool that facilitates revisions in real-time. Web development projects are more efficient for clients, managers, and dev teams by assigning tasks, setting priorities & deadlines all in one place.

With Webvizio, your team and your clients have the ability to share feedback and track bugs easily, and it features integrations with many popular apps, making it one of the best data annotation tools on the market.

  • Share feedback on websites and web apps
  • Manage multiple websites with task management
  • Leave comments on images and PDFs
  • Website review on mobile
  • Website bug tracker
  • Leave video feedback

Price: There’s a free forever plan with limited features and paid plans are $5.20/user/month.

7. Zipboard

Zipboard

zipBoard is a visual feedback and collaboration software for digital assets including PDFs, websites, web apps, images, videos, and SCORM files. The cloud-based platform has in-built visual review and annotation tools that allow internal and external stakeholders to easily leave contextualized feedback on digital assets in a centralized location. Some of the annotation features include highlighter, pencil, arrow, rectangle, screenshot, screen recording and many more.

  • Multiple content types, including PDFs, live URLs, images, videos, and zip files
  • Seamless integration with software like Slack, Jira, Microsoft Teams, Wrike, and Lambda Test
  • Both internal and external stakeholders can collaborate with ease
  • Organize tasks in Kanban or spreadsheet views

Price: 15-day free trial available for all plans (team, agency, and enterprise). The Team plan starts at $99.00 per month. Details of the pricing for all plans are available here .

Miro Visual Notes

Miro is a cloud-based whiteboard platform that can be used for brainstorming, ideation, project management and agile development. Miro Visual Notes feature offers powerful solutions for your annotation needs, from rich text formatting, pre-made templates to embedding content. Miro’s advanced text editor is also a time-saver if your company uses Miro as its main project management solution. You can easily embed any note into a Miro board and invite your colleagues to create, edit and collaborate with you.

  • Like in any other text editor, you can include bold, italics, underline, and strikethrough.
  • Let’s you link text to other objects on the board or to external websites.
  • Allows you to insert images into your notes.
  • You can create checklists to keep track of your tasks.
  • Invite multiple people to collaborate on the same notes at the same time.
  • Share and export your notes to a variety of formats, including PDF, Word, and Markdown.

Price: Miro has a free plan with limited capability, a starter plan at $8 per member/month and a business package at $16 per member/month.

Any questions about Hive or these annotation tools? Let us know at [email protected]

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list of annotation tools

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Deep Learning Guide: Choosing Your Data Annotation Tool

We all know what data annotation is. It’s a part of any supervised deep learning project, including computer vision. A common computer vision task, like image classification, object detection, and segmentation requires annotations for each and every image fed into the model training algorithm. 

You simply must get a good tool for image annotation. In this article, we’ll be checking out a few top picks that I’ve worked with throughout my career as a deep learning engineer. Even though they have the same end goal, each annotation tool is quite unique and has individual pros and cons. 

To compare them, let’s define a list of criteria that will help you choose a tool that works best for you, your team, and your project.

How to choose the right data annotation tool?

The criteria for choosing the right data annotation tool are as follows:

  • Efficiency,
  • Functionality,
  • Formatting,
  • Application,

There are a lot of images available to deep learning engineers nowadays. Annotations are manual by nature, so image labeling might eat up a big chunk of time and resources. Look for tools that make manual annotation as time-efficient as possible. Things like convenient user interface (UI), hotkey support, and other features that save our time and improve annotation quality. That’s what efficiency is about.

Functionality

Labels in computer vision can differ depending on the task you’re working on. In classification, for example, we need a single label (usually an integer number) that explicitly defines a class for a given image. 

Object detection is a more advanced task in computer vision. In terms of annotations, for each and every object you need a class label, and a set of coordinates for a bounding box that explicitly states where a given object is located within an image. 

Semantic segmentation requires a class label and a pixel-level mask with an outline of an object. 

So, depending on the problem you’re working on, you should have an annotation tool that provides all the functionality you need. As a rule of thumb, it’s great to have a tool that can annotate images for all kinds of computer vision tasks you might encounter.

Annotations come in different formats: COCO JSONs, Pascal VOC XMLs, TFRecords, text files (csv, txt), image masks, and many others. We can always convert annotations from one format to another, but having a tool that can directly output annotations in your target format is a great way to simplify your data preparation workflow, and free up a lot of time.

Data annotation tools

Application

Are you looking for a web-based annotation app? Maybe you sometimes work offline, but still need to do annotations, and would like a window app that can be used online and offline? These might be important questions in the context of your project.

Some tools support both window apps and web-based apps. Others might be web-based only, so you won’t be able to use them outside of a web browser window. Keep that in mind when looking for your annotation tool.

If you work with sensitive data, consider privacy issues: uploading your data to a 3rd-party web app increases the risk of a data breach. Will you take the risk, or go with a safer, local annotator? 

Price always matters. From my personal experience, most engineers in small / medium size teams tend to look for free tools, and that’s what we’ll focus on in this article. 

For a fair comparison, we’ll take a look at paid solutions too, to figure out if they’re worth it. We’ll look at the circumstances when paid solutions make sense, and actually generate additional value.

Overview of labeling tools

You won’t see “best” or “worst” in my reviews of each annotation tool. For each of us, the “best” tool is one that meets our individual requirements and circumstances.

I will describe top 5 annotation tools , hopefully you’ll be able to choose one for yourself. These tools have proven to have good performance, and they’re well known among deep learning engineers. I’ve had a chance to work with each of these tools, and I’m happy to share my experience with you. Let’s jump in!

LabelImg is a free, open-source annotator. It has a Qt graphical interface, so you can install it and use it locally on any operating system . The interface is very simple and intuitive, so the learning curve won’t be extremely steep.

LabelImg can output annotations in multiple formats , including Pascal VOC XMLs and YOLO’s txts. It can also output CSVs and TFRecords with a few additional steps. 

LabelImg

LabelImg supports hotkeys to improve the annotation process and make it more convenient. Users can also enjoy a LabelImg image verification feature.

Annotation tools LabelImg

LabelImg has one, but very important drawback – it only supports bounding boxes for annotations . It’s also worth mentioning that LabelImg is strictly a window-based app, with no browser support. If these limitations are ok for you, LabelImg is indeed a good candidate for an annotator for your project. 

For more detailed review, guided installation and annotation process presentation, I recommend watching this tutorial created by The AI Guy .

VGG Image Annotator (VIA)

VIA is another tool for image annotations that should be on your watch list. It’s a free, open-source solution developed by a team from Oxford University.

In contrast to LabelImg, VGG Image Annotator runs entirely in a browser window . Even though it’s a web-based app, users can work offline in most web browsers. The app fits in a light-weight HTML page.

list of annotation tools

VIA has a broad range of functionality. You can draw different regional shapes around objects. Not just bounding boxes, VGG image annotator also supports circles, ellipses, polygons, points and polylines.

Annotation tools VGG

VIA can also annotate video frames, audio segments, and video subtitles . If you want a universal, but simple tool, VIA might be a good choice.

It has basic keyboard shortcuts that speed up the annotation process. I personally love how hotkeys work in VIA. It’s extremely convenient and well-organized.

Final annotation files can only be exported in a limited number of formats : COCO JSONs, Pascal VOC XMLs and CSVs are the supported formats. To cast annotations to other types of formats, additional external transformations will be needed, so consider that when making a decision.

To try VGG Image Annotator, check out demonstrations with preloaded data. Here are some use cases that you can browse through:

  • Basic image annotation example
  • Annotations for a video
  • Audio Annotation
  • Video subtitle annotation

If you’re curious how an annotation process is performed in VIA, this guided tutorial by BigParticle.Cloud will give you a good overview.

Computer Vision Annotation Tool (CVAT)

Annotation tools CVAT

CVAT’s user interface (UI) was optimized based on feedback from many professional annotation teams. Because of that, CVAT is very well designed for image and video annotation.

You can start an annotation job from CVAT’s website, and work fully online in a web-based application . CVAT’s website has some limitations, though:

  • You can only upload 500 mb of data,
  • Only 10 tasks per user.

Luckily, you can install it locally, and even your work offline . Installation is nicely documented , all operating systems are supported. 

Supported shape forms include rectangles, polygons, polylines, points and even cuboids, tags and tracks. Compared to the previous annotators, CVAT supports annotation for semantic segmentation . 

The amount of supported annotation formats for export is impressive. Here’s a complete list as of March, 2021:

  • Pascal VOC (xmls)
  • Segmentation masks for Pascal VOC
  • YOLO (txts)
  • MS COCO object detection (jsons)
  • LabelMe 3.0
  • Market-1501

Teams will find CVAT especially useful, because it’s so collaborative . CVAT lets users create annotation tasks and split up the work among other users. Moreover, annotation jobs can be monitored, visualized and analyzed using elasticsearch logstash kibana . It’s always great to have a chance of control over the labeling process, visualize progress, and manage it based on monitoring results.

list of annotation tools

Shortkeys cover most common actions and help a lot in real annotation work.

Annotation tools shortkeys

Automated annotation using pre-trained models is available. Users can select a model from a model zoo , or connect a custom model.

It has some flaws. Like limited browser support for CVAT’s client. It works well only in Google Chrome. CVAT wasn’t tested and optimized for other browsers. That’s why you can get unstable operations in other web browsers, although not always. I don’t use Google Chrome, and see no significant drops in performance, just some minor bugs that don’t bother me.

To get a sense of what CVAT is and its UI, you can try an online demo on CVAT’s website or watch a video of an object annotation process by Nikita Manovich .

Visual Object Tagging Tool (VoTT)

Annotation tools Vott

Microsoft has come up with its own solution for data annotation – Visual Object Tagging Tool (VoTT). Free, open-source tool with a very good reputation among data scientists and machine learning engineers.

Microsoft states that “VoTT helps facilitate an end to end machine learning pipeline”. It does with three main features:

  • Its ability to label images or video frames;
  • An extensible model for importing data from local or cloud storage providers;
  • An extensible model for exporting labeled data to local or cloud storage.

There’s both a web application and a native app. Compared to competitors, any modern web browser can run the annotator web app. It’s definitely a competitive advantage for those teams who got used to a particular browser, and don’t want to change it. 

On the other hand, VoTT’s web app is not as light-weight as VIA’s. It needs a bit of time and resources to be loaded in a browser window.

Annotation tools Vott app

Another drawback of VoTT’s web app – it can’t access the local file system. The dataset needs to be uploaded to a cloud, which can be inconvenient.

Visual Object Tagging Tool will ask you to specify two connections : for import (a source connection), and for export (a target connection). Projects in VoTT are designed as a labelling workflow setup , and require a source and target connections to be defined. You can analyze the way VoTT treats and organises labeling jobs in the official docs . The overall structure is very well designed and organized.

Annotation shapes in VoTT are limited to only two types: polygons and rectangles. However, the library of supported formats for export is quite rich. It includes:

  • Generic JSONs;
  • Pascal VOC;
  • Microsoft Cognitive Toolkit (CNTK);
  • Azure Custom Vision Service.

Annotation tools Vott web app

There are several keyboard shortcuts that let users always keep one hand on the mouse and one on a keyboard while annotating. The most common general shortcuts (copying, pasting, redoing) also have full support in VoTT.

To try Visual Object Tagging Tool, go to VoTT’s web app and give it a spin. Another great source of information about VoTT are guided tutorials. This tutorial by Intelec AI is one of my favourites. Consider watching it if you want to know more about VoTT, its UI and features.

Supervisely

I promised to put in some paid alternatives, and here it is. Supervisely – an end to end computer vision lifecycle platform. 

Supervisely is not just an annotation tool, it’s a platform for computer vision product development . Functionally, it’s not limited to a single data annotation process. Instead, teams and independent researchers, with or without machine learning expertise, can build deep learning solutions for their needs. All of that is done in a single environment.

list of annotation tools

In terms of labeling, Supervisely lets you annotate not only images and videos , but also 3D point clouds (3D scenes built by complex sensors like LIDARs and radar sensors), and volumetric slices.

Annotation tools include conventional points, lines, rectangles and polygons. Plus, some pixel level instruments: 

  • Brushes to draw any shape on a scene using a mouse hold;
  • Erasers that remove unwanted pixels.

Annotation tools Supervisely

Instance and semantic segmentation can be boosted up with one of the most prominent features of Supervisely. It’s called AI Assisted Labeling . You only have to define a shape of an instance, and a built-in neural net will do the rest of the job, filling up the target pixels.

Supervisely AI Assisted Labeling

Images are taken from AI Assisted Labeling web page

Annotation jobs can be managed at different scales . Depending on the team, different roles can be assigned to users. Labeling job progress is transparent and trackable.

Annotation tools Supervisely

Annotated data can be immediately used to train a neural net. You can select a model from a Model Zoo with pretrained models or go with a custom model of your choice. Either way will work.

Model Zoo is very rich with pretrained models. All models from the zoo can be added to an account and used to retrain a new custom model, so you don’t need to worry about the data format that a particular neural net requires. Supervisely does all data preparation and transformation steps for you. You’ll just have to fit in the data.

Trained models can be deployed as API. Alternatively, model weights and source code can be downloaded to use in any other scenario.

Supervisely has many other cool features, all of which I won’t be able to cover in this article, as we’re focusing on annotation tools. In case you’d like to know more about this platform, there’s an official youtube channel . I encourage you to browse through their playlists, and watch videos about topics, functionality and features that interest you. You also can look at some use cases if you wish.

In terms of pricing , students and fellow data scientists can use Supervisely at no costs. Companies and enterprises should get in touch to request pricing details . Supervisely states that their service is used by more than 25,000 companies and researchers worldwide, including big names like Mazda, Alibaba group, or Basf.

Conclusions

To choose a data annotator for a deep learning project, you need to be thorough: there are overwhelmingly many solutions available. Not surprisingly, each tool has different pros and cons. By now, you should have a good sense of how they differ, and what to look for depending on your needs.

We’ve gone over five candidates for consideration, looking at them from five different perspectives: efficiency, functionality, annotation formatting, application type and, of course, pricing.

LabelImg, our first candidate, is a simple and light weighted annotator. It’s extremely intuitive. If you don’t need unnecessary complexity, and solve object detection tasks with labeling, might be interested in using LabelImg. It will do exactly what you need.

VIA covers some of the drawbacks of LabelImg. You can use a web app, there’s a broader range of shapes for labeling; not just rectangles but also circles, ellipses, polygons, points and polylines.

CVAT, in contrast, supports semantic segmentation. Its collaborative functionality will serve as a good basis for effective team work.

VoTT is the only web-based annotator, optimized to work with every modern web browser. It’s backed by Microsoft, and simply can’t be a bad product.

Supervisely is the only paid candidate we’ve considered. Experienced deep learning engineers will definitely benefit from the automation and rich functionality of Supervisely. The less experienced will enjoy how it simplifies the machine learning workflow.

Find and select the tool that fits your requirements. I hope this article will help you make a good choice.

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in the Loop

list of annotation tools

  • 10 of the best open-source annotation tools for computer vision
  • Best labeling tools

Here at Humans in the Loop   we know the importance of finding good image labeling and annotation tools in creating accurate and useful datasets. On our blog , you can find our Tools we love series where we deep dive into some of our favorite annotation tools as well as our picks for the best annotation tools here , here , and here . 

With the growth of the image annotation space, we have seen an upsurge in the availability of open source tools which enable anyone to label their images for free and to benefit from great functionalities. Keep reading to find out which are the 10 best open source tools for computer vision annotation!

1. Label Studio

list of annotation tools

Image source

Our absolute favorite open source labeling tool is Label Studio by Heartex Inc . We recently featured the enterprise version of the tool in our previous article because we were impressed by its versatility and advanced functionalities for active learning and collaboration. The open source version of the tool works with all types of data such as audio, text, images, videos and time series. 

It supports a wide array of annotation including image classification, object detection, semantic segmentation. It works with almost every type of data, such as audio, image, text, and HTML and has a unique configuration setup called Labeling Config where you can design your own customized UI. It has a variety of algorithm-driven automation features, including a pre-labeling option that can pre-label data based on an existing machine learning model. Finally, what we love about it is that it has a vibrant community of users and a very active slack channel where you can exchange tips or make requests to the team.

2. Diffgram

list of annotation tools

The second platform on this list is Diffgram ! Diffgram has also been featured before on our Tools we love series back when it was a paid platform. Since the spring of 2021, Diffgram has switched to being a fully open-source platform with optional paid managed services and enterprise support. We fully endorse the shift and we are very excited about its extended reach. 

What makes it stand out is that in addition to being an annotation platform, it also has various dataset and workflow management capabilities. It supports almost every single type of spatial annotation in both image and video including but not limited to bounding boxes, cuboids, segmentation, cuboids and keypoints. Its semantic segmentation feature also offers various tools such as auto-bordering, combo shapes, points-to-full polygon amongst many others! Additionally, its video annotation supports interpolation and sequence labeling such as event tracking and object tracking.

Best enterprise tools

Check out our selection of the 4 best paid annotation tools on the market!

V7 Automated polygon detection GIF

3. LabeIImg

list of annotation tools

LabelIMG is another very popular open source and free to use image labeling tool. In fact, it was the first labeling tool that we ever used back in 2017 and it facilitated our work a ton thanks to its simple and intuitive interface and the fact that it can be used offline in order to provide maximum data security. 

It can also be installed on almost every single operating system such Windows, Linux, Ubuntu and the Mac OS and its Python library can also be used in Anaconda or Docker. However, it only supports bounding boxes as the lone labeling method which is hence why it is a great first tool but often might not be enough for more complex projects. It can save the annotations as XML files in PASCAL VOC format as well as in the YOLO and CreateML formats.

list of annotation tools

The Computer Vision Annotation Tool (CVAT) is an open-source software developed by Intel. Whilst it does not have the most intuitive UI, it has very powerful and up-to-date features and functionalities and runs in Chrome. It still is among the main tools that both we and our clients use for labeling, given that it’s much faster than many of the available tools on the market. 

It offers object detection, image classification and image segmentation and annotation with boxes, polygons, lines and keypoints. CVAT even offers various automation features such as copying and propagating objects, object tracking and interpolation and automatic annotation, powered by TensorFlow OD API. Collaboration is easy in CVAT and one can split and delegate work. 

5. ImageTagger

list of annotation tools

ImageTagger is an open-source online platform for collaborative image labeling. Developed by Niklas Fiedler from the Department of Informatics at the University of Hamburg specifically for the needs of the Robot World Cup, this platform is designed so that the actual labeling procedure is as intuitive and quick as possible. 

It allows for image sets to be labeled with bounding boxes, polygons, lines and keypoints. It does offer some project management options and features that assist with QA such as image preloading, uploading existing labels, and label verification. Additionally, as it is focused on collaboration, it allows for large-scale collaboration on labeling of the image sets by splitting up labelers in teams.

Ultimate guide to annotation

Interested in understanding the different types of labeling that you can use for your projects? Check out our Ultimate Guide!

Traffic monitoring vehicles post segmentation example

LabelMe  is an open-source tool that is considered an industry classic. Built by the Massachusetts Institute of Technology in 2008 in order to build the canonical LabelMe datset, LabelMe can either be used online or offline . It can be run on Windows, Ubuntu and the Mac operating system along with Python launchers. LabelMe offers image and video annotation with polygons, boxes, circles, lines, keypoints and both semantic and instance segmentation. 

Additionally, it offers classification through its image flag annotation tool as well as a cleaning feature and has a customizable UI. It also allows for exporting semantic and instance segmentation in VOC and COCO-formats. However, it has almost no project management capabilities since it was not meant for collaborative labeling. Instead, there is an integration with Mechanical Turk for easy outsourcing of the manual labeling process.

list of annotation tools

The VGG Image Annotator also known as VIA was developed by the Visual Geometry Group at Oxford University. It is a rather simple annotation tool where images, audio and videos can be manually annotated – and it’s one of our favorites that we still regularly use. Very easy to use and install, it can be used as an offline application in any HTML capable browser. 

VIA can be labeled with shapes such as boxes, circles, ellipses, polygons, keypoints, and lines. VIA supports CSV and JSON exporting as well as limited project management functionality, such as allowing for multiple jobs to be set up for the annotator and progress to be tracked through a simple yet easy to use UI.

8. Make Sense

list of annotation tools

Make Sense is a relatively new open source annotation platform. Released in the summer of 2019 by Piotr Skalski , Make-sense has an amazing UI and there are no-frills when it comes to annotating, with additional object detection and image recognition capabilities. To get started, go to their website, drag and drop an image and you can start annotating immediately. They do not store images online and hence there are no privacy concerns. 

Make Sense supports bounding boxes, keypoints, lines and polygons and even uses AI models to automate some of the annotation such as an SSD model pretrained on the COCO dataset, and the PoseNet model, which can be used to estimate the pose of a person and key body in an image or video. Currently, Make Sense does not have any project management capabilities or an API.

9. COCO Annotator

list of annotation tools

COCO Annotator is created with Vue.js , an open-source model–view–view model front end JavaScript framework for building user interfaces and single-page applications, by Justin Brooks. It is a web-based image segmentation tool aimed to help develop and train object detection, localization, and keypoints detection models. 

Labeling datasets can be done with free-form curves, polygons and keypoints as well as other features to label image segments, track object instances, and even allows for the labeling objects with disconnected visible parts in a single instance, all while efficiently storing and exporting annotations in the COCO format, hence the name. COCO annotator also supports annotating images with semi-trained models and has advanced selection tools such as DEXTR, MaskRCNN and Magic Wand. Finally, it offers a user authentication system for security as well.

Types of annotation

  • Bounding box annotation
  • Polygon annotation
  • Keypoint annotation
  • Semantic segmentation
  • Video annotation
  • 3D annotation

Shopper tracking post-annotation example

10. Dataturks

list of annotation tools

Our final entry is an open-source platform that is not updated anymore. Dataturks used to be a paid service which was purchased by Walmart in 2018 and the development of the labeling platform has since stopped and it is now freely available on GitHub. It is completely free to use despite several allusions to paid extras when using the software. It seems to not have been developed for a while now but still is a solid open-source data annotation tool. It allows for teams to work asynchronously on various annotation types including images, videos, text and NER. 

With image annotation, it allows for free form polygons to be drawn to generate image masks for segmentation, as well as supports image classification through polygonal labels and bounding box labels. It even has a visual object tracking feature where one can draw a bounding box around a subject at certain times in the video and the tool will automatically do the interpolation between these points. Dataturks can export in VOC, Tensorflow and Keras formats.

Hope this was helpful! If you are working on an AI project and are currently reviewing which tool might be the most appropriate for it, get in touch with us and we would be happy to have a call and advise you on the best way to build your pipeline. We also offer dataset collection, labeling and annotation, as well as model validation services!

Annotation tools for building datasets

A list of the best open-source annotation tools for labeling data., use this form to add new tools to the list..

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The Ultimate Guide to Image Annotation: Techniques, Tools, and Best Practices

Priyanka kumari, 1. what is image annotation, definition and importance.

Image annotation is the process of adding descriptive labels or markings to images, which are typically used for training machine learning models, especially in the field of computer vision.

These labels provide essential information to the models, allowing them to recognize and understand objects, patterns, or features within the images. Image annotation is a crucial step in the development of AI and machine learning systems, and it serves several important purposes:

1. Training Machine Learning Models

Image annotation is fundamental for supervised learning, where models learn from labeled data. By associating labels or annotations with images, the models can understand and differentiate various objects, shapes, or characteristics in the images, making them capable of making accurate predictions or classifications.

2. Object Detection and Recognition

Image annotation is essential for tasks like object detection and recognition. It allows models to identify and locate specific objects or elements within images, enabling applications such as autonomous vehicles, facial recognition, and medical image analysis.

3. Segmentation and Masking

Image annotation can involve creating detailed segment masks that precisely outline objects or regions of interest within an image. This is crucial for tasks like image segmentation, where models need to separate objects from the background or distinguish different parts of an image.

4. Quality Control

Proper image annotation ensures the accuracy and consistency of training data. Without high-quality annotations, machine learning models may produce unreliable results. Therefore, rigorous quality control and validation are often carried out during the annotation process.

5. Customization and Adaptation:

Image annotation allows customization of machine learning models to suit specific applications or industries. For instance, annotating images of medical scans with specific medical conditions allows the development of models for disease diagnosis.

6. Data Augmentation:  

Annotated images can be used for data augmentation, where variations of the original dataset are created by modifying or augmenting the annotated images. This helps improve model robustness and generalization.

7. Transfer Learning:

Annotated data can be used to pre-train models, which can then be fine-tuned for specific tasks. This is particularly valuable when labeled data is scarce or expensive to acquire.

Use Cases in AI and Machine Learning

1. healthcare, - covid-19 diagnosis:.

Annotated medical scans, such as CT scans and MRIs, were crucial for developing AI/ML systems to detect COVID-19 based on scan results, helping correlate patient scans with their COVID-19 status.

- Face Mask Detection:

AI-based solutions were needed to detect whether individuals were wearing masks, especially during the pandemic, to enforce safety measures.

Face Mask Detection

- Tumor Detection:  

Annotated images depicting tumor regions help machine learning systems learn patterns for diagnosing tumors, aiding radiologists and doctors in the detection process.

2. Autonomous Vehicles

- autonomous driving:.

Annotated images and videos in large datasets enable self-driving vehicles to identify and localize important elements for safe navigation, such as other vehicles, traffic signs, driving lanes, and more.

- Traffic Flow Analysis:

Annotated data from surveillance cameras can provide insights into traffic flow, congestion, and road conditions, aiding traffic engineers in managing roadways.

Traffic Flow Annalysis

- Parking Occupancy Detection:

Annotated data helps machine learning systems predict available parking slots, enhancing parking guidance and information systems.

3. Agriculture

- disease and pest detection:.

Annotated data is used to localize and classify infected plants, allowing AI models to predict the presence of diseases and pests with location and severity information.

- Crop and Yield Monitoring:

Annotated datasets provide insights into crop growth, ripeness, and statistics, reducing the need for manual surveillance and helping farmers take informed actions.

- Livestock Health Monitoring:

Data characterizing various livestock animals, their health status, and diseases within them enables the development of systems to monitor livestock health and assist farmers in tracking the well-being of their animals.

Agriculture Image Annotation

In all these use cases, image annotation plays a crucial role in training machine learning models and computer vision systems.

It provides the labeled data necessary for these systems to understand and interpret visual information, making them effective tools in various industries and applications. The accurate and detailed annotations in these domains help drive innovation, improve safety, and enhance efficiency.

2. Types of Image Annotation

I. bounding box annotation.

Bounding boxes are a fundamental technique in computer vision that involves drawing rectangular boxes around objects of interest within an image. These boxes are widely used for various tasks, including object detection and localization. Bounding boxes provide a simple yet effective way to define the spatial location of target objects within an image.

Bounding Box Annotation

Typically, a bounding box is defined by its coordinates in the image. The two common representations are:

1. (x1, y1, x2, y2): This representation uses the coordinates of the upper-left corner (x1, y1) and the lower-right corner (x2, y2) of the rectangular box to specify its position. It is a straightforward way to define the box's location.

2. (x1, y1, width, height): Alternatively, a bounding box can be defined by the coordinates of the upper-left corner (x1, y1) and its width (w) and height (h). This representation provides a compact way to specify the box.

The primary purpose of bounding boxes is to aid in object detection and localization tasks. By drawing boxes around objects of interest, computer vision systems can identify and locate these objects within an image.

This is crucial in applications like image classification, object recognition, and autonomous driving, where the precise location of objects is essential for decision-making.

Bounding boxes offer several advantages:

1. Ease of Annotation: Annotating images with bounding boxes is relatively straightforward and quick. Annotators can draw boxes tightly around object edges, making it a practical choice for large datasets.

2. Object Localization: Bounding boxes provide precise information about the location of objects in an image. This information is crucial for various computer vision tasks, such as tracking and counting objects. However, bounding boxes also have limitations:

1. Inclusion of Background: Depending on the type and position of the object, the box might include a significant amount of background pixels that do not belong to the object. This is especially true when objects are close to each other or when they have irregular shapes.

2. Rotation Challenge: Bounding boxes are not ideal for objects that are rotated or have irregular orientations. In such cases, a rotated rectangle format that includes the angle of rotation in addition to the coordinates can be more suitable.

Bounding boxes are a commonly used and effective annotation technique in computer vision. They provide a simple and efficient way to define the spatial location of objects within an image.

While they have their limitations, bounding boxes remain a fundamental tool for various object detection and localization tasks, offering a balance between ease of annotation and precise object localization.

II. Polygon Annotation

Polygon annotation is a technique used in computer vision to outline the shape of objects within an image by defining a series of connected vertices. Unlike bounding boxes, which use rectangular shapes, polygon annotation provides a more precise boundary for objects and is commonly employed in tasks that require detailed object segmentation.

Polygon Annotation

In this method of image annotation, annotators draw polygons around objects, accurately capturing their intricate shapes and sizes. The process involves identifying the object's borders within the frame with a high level of accuracy.

This precise annotation technique is crucial for identifying various objects, such as street signs, logos, and facial features in sports analytics. It enables detailed recognition and analysis of these objects within images.

Polygons offer several advantages over bounding boxes:

1. Precision: Polygons allow annotators to include only the pixels that belong to the object, resulting in a more accurate representation of the object's shape. This precision is vital for tasks where the exact object boundaries are essential.

2. Versatility: Polygon annotation is versatile and can be applied to objects of various shapes, making it suitable for annotating complex and irregularly shaped objects. Unlike bounding boxes, polygons can accurately capture the contours of objects that do not fit well within rectangular boundaries. However, polygon annotation also has its challenges:

1. Complexity: Drawing polygons requires more effort and time compared to drawing bounding boxes. The process involves specifying a sequence of x, y coordinates for every point that makes up the polygon. This complexity can slow down the annotation process, especially when dealing with a large dataset.

2. Tool Support: The annotation tool used must support polygons and, in some cases, holes within polygons. For instance, annotating objects like donuts or pretzels requires the tool to handle interior and exterior coordinates separately. Ensuring proper support for these features is essential for accurate annotation.

3. Overlap Handling: When polygons overlap, annotators need to consider the order of objects and which polygon appears on top. Managing overlapping polygons can be challenging and requires careful attention to detail.

Polygon annotation is a powerful technique for object segmentation in computer vision tasks. It offers a high level of precision, making it ideal for tasks that demand accurate object boundaries. While it may be slower to annotate compared to bounding boxes, polygon annotation is essential for applications where detailed object shapes are critical for accurate analysis and recognition.

III. Semantic Segmentation

Semantic segmentation is a high-precision image annotation technique used in computer vision that assigns a pixel-level label to each pixel in an image. It involves categorizing each pixel into different object classes or regions, providing a detailed and pixel-wise understanding of the image's content.

This pixel-wise labeling allows for precise object segmentation, making it a powerful tool for various applications in computer vision and artificial intelligence.

Semantic Segmentation

Key points about semantic segmentation include

1. Pixel-Wise Annotation: Semantic segmentation is a pixel-wise annotation method, meaning that each pixel in an image is assigned to a specific class or category. These classes can represent objects, regions, or different semantic meanings within the image. For example, classes might include "pedestrian," "car," "bus," "road," "sidewalk," and more. Each pixel carries a semantic label that indicates its classification.

2. Detailed Object Understanding: This technique provides a highly detailed understanding of an image, as it categorizes every pixel. Unlike other annotation methods that primarily focus on object detection or localization, semantic segmentation's primary goal is to interpret and label the pixels within the image.

3. Applications: Semantic segmentation is commonly used in scenarios where environmental context is crucial for decision-making. It is vital in applications such as self-driving cars, robotics, and scene understanding. In self-driving cars, for instance, the technology helps the vehicle understand the road, identify obstacles, and make safe driving decisions.

4. Output Format: The output of semantic segmentation is typically a pixel-wise mask or map, often in the form of a PNG image where each color corresponds to a specific class. Additionally, it can be represented in other formats like JSON files with bitmap objects encoded as base64 strings.

1. Ultra-Precision: Semantic segmentation provides an extremely high level of precision since every pixel in the image is assigned to a specific class. This precision is invaluable for tasks where object boundaries and fine-grained information are crucial.

2. Environmental Context: It is particularly useful when understanding the broader context of an image is essential. In applications like autonomous vehicles, semantic segmentation helps in making informed decisions by comprehensively labeling the surroundings.

1. Labor-Intensive: The process of segmenting an image at the pixel level can be labor-intensive and time-consuming. Annotators need to classify each pixel accurately, which is a more demanding task compared to methods like bounding boxes or polygons.

2. Tool Support: Annotating images with semantic segmentation requires specialized tools capable of handling pixel-wise annotation. Superpixel annotation tools, which divide the image into larger tiles based on edge detection, are often used to simplify the annotation process.

Semantic segmentation is a powerful technique for pixel-wise annotation in computer vision. It enables precise object segmentation and is particularly valuable in applications that demand a granular understanding of an image's content, such as autonomous vehicles and scene analysis. While it requires substantial effort, the level of detail it provides is unmatched in many computer vision tasks.

IV. Landmark Annotation

Landmark annotation is a data annotation technique used in computer vision to identify and mark key points or landmarks on objects, primarily on human faces and bodies, but it can also be applied to other objects. These landmarks serve as reference points that allow for precise tracking, recognition, and analysis of objects and their movements.

Key Points of Landmark Annotation

1. Facial Landmarks: Landmark annotation is prominently used for facial landmark detection. It involves annotating critical points on a human face, such as the eyes, eyebrows, nose, lips, and the oval shape of the face. These landmarks play a crucial role in facial recognition, analysis, and various applications like face morphing and replacement.

2. Object Landmarks: Beyond faces, landmark annotation can also be applied to other objects and body parts. For instance, it is used in human pose estimation, where key points on the human body are annotated to create a 2D or 3D skeleton, enabling accurate assessment of human posture and movement.

3. Applications of Landmark Annotation: Landmark annotation finds applications in various domains, including:

- Facial Gesture Recognition: It helps identify and understand human facial expressions and emotions, which is vital in applications like mood analysis and sentiment recognition.

- Human Pose Estimation: Landmarks are used to create skeletal structures, enabling fitness apps to assess exercise correctness and enhancing safety in industrial settings by detecting incorrect body postures.

- Counting Small Objects: Landmark annotation can be used for counting small objects in bird's-eye-view images, making it valuable in applications like monitoring parking lots or forests from aerial views.

Industries Where Landmark Detection Is Used:

The application of landmark annotation is diverse and spans across several industries, including:

Industries Where Landmark Detection Is Used

- Sports : Fitness apps use landmark annotation to assess the correctness of users' movements and poses during exercise.

- Gaming: Landmark annotation is used to simulate human-like movements in video games, enhancing the realism of character animation.

- Deepfakes: Landmark annotation is a fundamental component of deepfake technology, allowing for face replacement and realistic morphing.

- Augmented and Virtual Reality: It helps create lifelike characters and creatures for immersive AR and VR experiences.

- Cinema: In the film industry, landmarks enable the creation of lifelike and natural movements for computer-generated creatures and characters.

- Military: Drones equipped with landmark detection can be used for monitoring weapon reserves and observing military activities.

- Safety and Security: It can be applied to monitor crosswalks for self-driving cars and enhance security and surveillance by detecting unusual human activity and changing conditions.

Challenges in Landmark Annotation

While landmark annotation is a valuable tool, it comes with its own set of challenges, including:

1. Limited Training Data: Successful machine learning projects relying on landmark annotation require extensive and diverse training datasets. This may involve capturing images and videos from various angles to enable more accurate recognition.

2. Background and Illumination: Real-world photos and videos often come with challenges related to background and illumination, making it harder to annotate landmarks accurately. Issues like overexposure, shadows, and cluttered backgrounds can affect recognition.

3. Hidden Object Parts: Landmark annotation may become challenging when object parts are hidden, bent, twisted, or turned. Inaccurate or incomplete landmark settings can lead to poor recognition of movements, gestures, and emotions.

Landmark annotation is a versatile technique used to mark key points on objects, primarily human faces and bodies, to enable precise tracking, recognition, and analysis. Its applications span across multiple industries, enhancing tasks such as facial recognition, pose estimation, gaming, and safety. Despite its advantages, challenges related to data volume, background, and occlusion need to be addressed for accurate and reliable results.

V. Line Annotation

Line annotation is a data annotation technique used in computer vision to create and mark lines and splines in images. It serves the purpose of delineating boundaries or highlighting specific features within an image. Line annotation is particularly useful when a region that needs to be annotated can be conceptualized as a boundary, but it is too small or narrow for a bounding box or other types of annotation to be practical.

Key Points of Line Annotation

1. Creating Boundaries: Line annotation is primarily employed to define boundaries or regions within an image. It involves drawing lines or splines along edges or features that need to be delineated. These annotations can help machine learning models understand and differentiate between different parts of an image.

2. Use Cases:

- Warehouse Robotics: Line annotation is used in training robots that operate in warehouse settings, helping them recognize differences between various sections of a conveyor belt or identify pathways for navigation.

- Autonomous Vehicles: Line annotation plays a critical role in autonomous driving systems as it enables the recognition of lanes, lane boundaries, and road markings. This information is vital for autonomous navigation and safety.

3. Precise Recognition: Line annotations contribute to the precise recognition of features within images. They enable computer vision systems to understand the spatial layout and relationships between objects, enhancing their ability to make informed decisions.

Industries Where Line Annotation Is Used

Line annotation finds application in several industries, each benefiting from its capability to define boundaries and recognize critical features:

- Autonomous Vehicles: Line annotation is fundamental for self-driving cars to navigate and stay within lanes, ensuring safe and efficient transportation.

- Manufacturing and Warehousing: In manufacturing and logistics, line annotation assists robots in identifying paths, handling materials, and maintaining efficient operations.

- Agriculture: Agricultural equipment relies on line annotation for tasks like crop monitoring and automated harvesting, helping optimize yields and resource usage.

- Retail and E-commerce: In warehouses and distribution centers, line annotation supports automated systems in managing inventory and ensuring smooth product flow.

Retail and E-commerce

- Geospatial Analysis: Line annotation is used in geospatial applications to define boundaries and features on maps, aiding in land use planning and environmental analysis.

Challenges in Line Annotation

While line annotation is a valuable tool in computer vision, it does present some challenges:

1. Annotation Subjectivity: Line annotation can sometimes be subjective, as different annotators may draw lines slightly differently. Achieving consistency in annotation can be a challenge.

2. Complex Shapes: When annotating complex or irregular shapes, it may be challenging to accurately represent the boundaries using simple lines or splines.

3. Varying Image Quality: Images with low resolution or high levels of noise can make line annotation more difficult, potentially leading to less accurate results.

Line annotation is a critical technique in computer vision, facilitating the precise definition of boundaries and features within images. Its applications are diverse, spanning across industries where the recognition of lines, lanes, and pathways is essential for the efficient operation of AI systems.

Despite potential challenges related to subjectivity and image quality, line annotation remains a valuable tool for enhancing the capabilities of machine learning models in various domains.

VI. 3D Annotation

3D annotation is a data annotation technique applied to three-dimensional data, allowing depth, distance, and volume to be considered when labeling and categorizing objects or structures in a 3D space.

While the more common forms of annotation are performed on 2D images, 3D annotation is essential when dealing with volumetric data or when additional dimensions are critical for understanding the content. Here's an overview of 3D annotation and its applications:

Annotation 3D Images

3D Annotation in Different Contexts

1. Medical: 3D annotation is commonly used in the medical field for analyzing 3D scans like CT (computed tomography) and MRI (magnetic resonance imaging). It enables the precise labeling and identification of anatomical structures and abnormalities in the three-dimensional space of the body.

2. Geospatial: In geospatial applications, 3D annotation helps in the detection of three-dimensional structures on Synthetic-Aperture Radar (SAR) imagery. This is particularly valuable for tasks like urban planning, terrain analysis, and disaster response.

3. Automotive: The automotive industry relies on 3D annotation for tracking and recognizing vehicles and objects in LiDAR (Light Detection and Ranging) point cloud data. LiDAR technology is instrumental in autonomous vehicles for perceiving their surroundings.

4. Industrial: 3D annotation is used in industrial settings to detect anomalies and quality control issues in 3D scans of products, ensuring that they meet the desired specifications.

5. Agriculture: It plays a role in agriculture by powering harvesting robots for fruit picking. The three-dimensional perception of the environment allows robots to accurately identify and pick ripe fruits.

6. Retail: In the retail sector, 3D annotation is employed for detecting gestures and poses in three-dimensional space. This technology is essential for virtual reality (VR) and augmented reality (AR) applications, enhancing the interaction between users and virtual environments.

3D Annotation of Images

The Pros of 3D Annotation

1. Enhanced Dimensionality: 3D annotation considers additional dimensions, providing valuable insights, especially when 2D visual data is insufficient. This is particularly important in challenging conditions like fog, darkness, occlusion, or scenarios where depth and volume perception are critical.

2. Approximation on 2D Data: While 3D annotation is best suited for true 3D data, it can also be approximated on 2D data. For example, sequences of DICOM (Digital Imaging and Communications in Medicine) slices can be used for 3D medical segmentation, and cuboid estimation can be applied to flat images based on perspective.

The Cons of 3D Annotation

1. Time-Consuming: Annotating 3D data is a time-consuming task, and it requires advanced tools and software that can efficiently handle large volumes of three-dimensional data.

2. Quality Limitations: The quality of 3D annotation can be affected by the nature of the data source. LiDAR data, for example, provides high precision for objects close to the source but may suffer from point dispersion as objects move further away, potentially affecting the quality of the annotation.

3D annotation is a valuable technique that considers depth and volume when annotating objects or structures in a three-dimensional space. Its applications span various industries, offering insights and precise recognition capabilities in situations where 2D data falls short. While it has its challenges, it plays a crucial role in fields where three-dimensional understanding is paramount.

VII. Video Annotation

Video annotation is the process of adding labels, tags, shapes, or timestamps to video data to enhance its content and guide user actions. This technique is essential for various applications, particularly in the field of artificial intelligence, where it is used to train computer vision models and improve video content for viewers. Here's an overview of video annotation and its benefits:

Video Annotation in Machine Learning

In the context of artificial intelligence, video annotation plays a crucial role in labeling and categorizing data to teach computer vision models how to recognize specific objects, actions, or situations in videos. For example, a dataset containing videos of cats and dogs can be annotated with labels to train a computer vision model to distinguish between these animals. Video annotation serves as the foundation for supervised learning models, allowing them to recognize patterns and objects.

Role of Video Annotation

Video annotation is used for a variety of purposes, including:.

1. Detection: Annotating videos to train AI models to detect objects or specific features in video footage. This is valuable for applications such as detecting cars on the road, identifying road damage, or spotting animals in wildlife videos.

2. Tracking: AI models can track objects in video footage and predict their future positions. Object tracking is essential for tasks like monitoring pedestrian or vehicle movement for security purposes.

3. Location: Training AI to locate and provide coordinates for objects within video footage. This can be applied to tasks such as monitoring parking space occupancy or coordinating air traffic.

4. Segmentation: Creating different classes and training AI models to recognize and categorize different objects or actions in video content. For example, video annotation can be used to identify and count ripe and unripe berries in agricultural footage.

Benefits of Video Annotation

Video annotation offers several advantages over annotating individual images:.

1. Interpolation: Video annotation tools allow for the annotation of keyframes at the beginning and end of a video sequence. The tool can then interpolate annotations for the frames in between, saving time and effort.

2. Temporal Context:  Videos provide temporal context, allowing AI models to understand object movements and changes over time. This is particularly valuable for tasks that involve motion and action recognition.

3. Better Data: Annotating videos provides AI systems with more data to work with, resulting in more accurate model training and recognition.

4. Cost-Effective: More data points can be obtained from a single video compared to a single image. Focusing on selected keyframes reduces the time required for annotation.

5. Real-World Applications: Annotated videos more accurately represent real-world scenarios and can be used to train more advanced AI models, leading to a wide range of computer vision applications across various industries.

3. Techniques for Image Annotation

Manual image annotation is a fundamental technique in computer vision and machine learning, where human annotators carefully select regions or objects within an image and provide descriptive labels or metadata for those specific parts. This process is essential for training machine learning algorithms, building image recognition models, and enhancing the understanding of visual data. Here's an elaborate exploration of manual image annotation:

I. Manual Annotation Process

The manual image annotation process involves human annotators who are tasked with analyzing raw, unlabeled visual data, such as photographs, videos, or other types of images. These annotators follow specific guidelines, rules, or specialized data annotation methodologies to identify and describe objects, regions, or elements within the images. The key steps in manual annotation are as follows:

Manual Annotation

- Data Selection: Annotators are provided with a batch of raw image data. This data may come from various sources, including surveillance footage, medical images, satellite imagery, or any domain where visual data analysis is required.

- Data Annotation Guidelines:  Annotators receive detailed instructions on how to classify or label the visual content. Guidelines may specify the types of objects or regions of interest to be annotated, the format for providing annotations (text, labels, bounding boxes, polygons, etc.), and any specific rules or criteria for the task.

- Annotation Process: Annotators manually review each image and select specific regions or objects within the image. They then provide written descriptions, labels, or other metadata to describe these regions. The annotations could range from simple object labels to more complex descriptions, such as fine-grained attributes.

Annotation Strategies

Manual image annotation can involve various strategies, depending on the complexity of the task and the level of detail required:

- Bounding Box Annotation: This is one of the simplest and most common manual annotation strategies. An annotator draws rectangles (bounding boxes) around objects or regions of interest within the image and provides labels for these bounded areas. Bounding box annotation is widely used in object detection tasks.

- Polygon Annotation: Similar to bounding boxes, polygon annotations involve outlining objects or regions, but with more detailed shapes. This approach is often used in scenarios where the precise boundaries of objects need to be defined, such as in fine-grained image segmentation tasks.

-  Semantic Segmentation: In semantic segmentation, annotators manually classify each pixel in an image, assigning a label to every pixel. This is a more intricate and time-consuming annotation method but is valuable for tasks that require precise delineation of object boundaries and fine-grained image understanding.

Use Cases and Applications

Manual image annotation plays a pivotal role in various applications and industries, including:

- Object Detection: Manual annotation is crucial for training object detection models. Annotators mark objects in images with bounding boxes or other annotations, allowing algorithms to learn to identify and locate objects within images.

- Image Segmentation:  For tasks like medical image analysis or autonomous driving, manual annotation is used to segment images into regions of interest. Precise segmentation helps identify and analyze specific areas within images.

- Content Moderation: In social media and content platforms, manual annotation is employed to label or flag content that violates community guidelines, ensuring a safe and appropriate user experience.

- Medical Imaging: Manual annotation is extensively used in the healthcare sector to label specific structures or anomalies in medical images, aiding in the diagnosis and treatment of various conditions.

Medical Imaging

Quality Control and Verification

Ensuring the accuracy and consistency of manual annotations is critical. Quality control measures may include inter-annotator agreement (multiple annotators reviewing the same data), review by domain experts, and feedback loops to improve annotation guidelines and accuracy over time.

Manual image annotation is a labor-intensive but essential technique for enhancing the understanding of visual data and training machine learning algorithms.

It involves human annotators selecting and describing objects or regions within images, and it is used in various applications to improve image recognition, content moderation, and data analysis in fields ranging from computer vision to healthcare.

The choice of annotation strategies, such as bounding boxes, polygons, or semantic segmentation, depends on the specific requirements of the task at hand.

II. Semi-automatic Annotation

Semi-automatic image annotation is a technique that combines manual annotation by human annotators with automated processes, particularly relevance feedback, to enhance the annotation of digital images. This approach aims to strike a balance between labor-intensive manual annotation and fully automated annotation methods.

In the context described in your provided content, semi-automatic image annotation is used to improve image retrieval systems and the quality of annotations by integrating the annotation process with relevant feedback. Here is an elaborate exploration of semi-automatic image annotation:

Semi-automatic Annotation

The Semi-Automatic Annotation Process

The semi-automatic annotation process combines the efforts of human annotators and machine learning techniques. The key steps involved in this process are as follows:

- Initial Manual Annotation: The process begins with a set of raw, unlabeled images. Human annotators are responsible for manually selecting regions or objects within these images and providing initial annotations, such as keywords or labels. These manual annotations serve as a foundation for the subsequent stages.

- Relevance Feedback: After the user submits a query for image retrieval, the system retrieves images based on the query, and these retrieved images are presented to the user. Users can then provide relevant feedback by indicating which images are relevant and which are not. This feedback helps improve the retrieval results.

- Integration of Relevance Feedback and Annotation: The user's feedback not only enhances the retrieval results but also contributes to the image annotation process. Images that the user marks as relevant receive automatic annotations related to the query keyword. This means that the initial manual annotations provided by human annotators are expanded upon based on user feedback.

 - Automatic Keyword Updates: The system assigns an initial weight to each query keyword associated with an image. This weight can be increased when users confirm relevance through feedback or decreased when images are deemed irrelevant. If the keyword's weight drops below a certain threshold, it may be removed from the image's annotation. This results in a dynamic set of keywords and their associated weights for each image.

Semi-automatic image annotation is especially valuable in applications where a large volume of images needs to be annotated to improve retrieval accuracy, and where user feedback plays a significant role. Some of the applications and use cases include:

- Image Retrieval: Semi-automatic annotation enhances image retrieval systems by allowing users to actively contribute to the annotation process through relevance feedback. This leads to improved retrieval results and a more user-focused experience.

- Personal Image Management: It can be applied in personal image management systems, where users are encouraged to annotate and organize their photo collections based on their preferences and feedback.

- Multimedia Databases: In the context of multimedia databases, semi-automatic annotation supports users in searching and retrieving images and multimedia content with greater accuracy.

- Content Moderation: For online platforms and social media, semi-automatic annotation can be used for content moderation by enabling users to flag or label images for relevance, ensuring that inappropriate content is appropriately managed.

Benefits and Challenges

Semi-automatic image annotation offers several advantages:

- User Involvement: It encourages users to actively participate in the annotation process, enhancing the relevance and quality of annotations.

- Efficiency: It strikes a balance between manual and automatic annotation, allowing for quicker expansion of the annotation database.

 - Dynamic Annotation: Keywords and their weights can be updated dynamically based on user feedback, leading to an adaptive annotation system.

However, it also comes with challenges, including the need to make relevant feedback more discoverable and user-friendly, as well as ensure the accuracy of keyword assignments based on feedback.

Semi-automatic image annotation is a practical approach that harnesses the power of both human expertise and machine learning to improve image retrieval and annotation systems. By combining the benefits of manual and automated processes, it offers an efficient and adaptive solution for managing large image databases.

III. Automatic Annotation

Automatic image annotation, also known as automatic image tagging or linguistic indexing, is a computer vision technique that aims to assign metadata, such as captions or keywords, to digital images automatically. This process is crucial for image retrieval systems, which help organize and locate images within a database based on user-defined queries. Here's an elaborate exploration of the automatic image annotation process and its significance:

Multi-Class Image Classification

Automatic image annotation can be viewed as a specialized form of multi-class image classification. However, it is distinct in that the number of possible classes is exceptionally large, often equivalent to the size of the vocabulary used for annotation. For example, a database of images might have a vocabulary of thousands or even millions of keywords or phrases that can be assigned to each image.

Automated Image Annotation

Machine Learning Techniques

Automatic image annotation relies on machine learning techniques to automatically generate annotations for images. The process typically involves two main components:

- Image Analysis: In this step, feature vectors are extracted from the images. These feature vectors capture visual information, such as color, texture, shape, and object detection. These features provide a numerical representation of the image content.

- Training Annotation Words: Machine learning models are trained using pairs of image feature vectors and corresponding annotation words or phrases. The model learns to identify correlations between image features and the textual annotations provided during the training process.

Correlation Learning: Early methods of automatic image annotation focused on learning the correlations between image features and training annotations. These methods attempted to establish relationships between visual content and textual descriptions.

Machine Translation Techniques: As the field advanced, machine translation techniques were employed to bridge the gap between the textual vocabulary (words and phrases) and the "visual vocabulary" found in images. This involved mapping visual features to textual annotations, thereby enabling automatic annotation of new images based on the correlations learned during training.

Classification Approaches:   More recent approaches to automatic image annotation often rely on classification techniques. Machine learning models, such as deep neural networks, are trained to predict the most relevant keywords or phrases for an image. These models are capable of handling a vast vocabulary of potential annotations.

Relevance Models: Some automatic image annotation systems use relevance models to assess the significance of various annotations to an image. These models consider not only the presence of an annotation but also its relevance and importance to the image's content.

Advantages of Automatic Image Annotation

- Natural Querying: Automatic image annotation allows users to specify queries in a more natural and user-friendly manner. Users can search for images based on the textual descriptions or concepts they have in mind, making it easier to find relevant images.

- Efficiency: Unlike content-based image retrieval (CBIR), where users often need to rely on low-level features like color and texture, automatic image annotation provides a more efficient and human-centric way to search for images. Users can describe what they are looking for in their own words.

- Scalability: Automatic image annotation helps address the challenges of handling large and constantly growing image databases. Manually annotating images is time-consuming and costly, making it impractical for managing extensive image collections.

Automatic image annotation is a powerful computer vision technique that leverages machine learning to assign metadata to digital images automatically. It allows for more user-friendly and efficient image retrieval, making it a valuable tool for managing and searching through vast image databases, especially in the era of big data and rapidly expanding digital content.

IV. Crowdsourcing

Crowdsourcing image annotation is a powerful technique for efficiently and cost-effectively labeling and annotating large volumes of image data. It involves outsourcing image annotation tasks to a distributed and diverse group of remote workers, often referred to as the "crowd." Here's an elaborate overview of the key aspects and benefits of crowdsourcing image annotation:

Cost-Effectiveness

Crowdsourcing image annotation is cost-effective because it leverages a global workforce to annotate images, eliminating the need to hire and maintain an in-house team. The cost savings can be substantial, particularly for businesses with large image datasets. By paying workers on a per-task basis, you can control costs more effectively and avoid the overhead associated with full-time employees. This is especially beneficial for startups and smaller organizations with budget constraints.

Image annotation is a time-consuming process, and crowdsourcing can significantly expedite the task. With numerous remote workers annotating images simultaneously, projects can be completed in a fraction of the time it would take in-house. This rapid turnaround is critical for various applications, such as real-time object detection, medical image analysis, or content moderation. Crowdsourcing can be particularly valuable when time-sensitive image labeling is required.

Scalability

Crowdsourcing provides unparalleled scalability. Whether you have a few hundred images or millions, you can easily adjust the workforce to meet your project's needs. This flexibility is crucial when dealing with fluctuating workloads or large-scale projects. For instance, Google's Quick Draw dataset, consisting of over 50 million images, demonstrates how crowdsourcing can be used to scale annotation efforts effectively.

Crowdsourcing enables access to a diverse pool of annotators from various backgrounds, cultures, and demographics. This diversity is essential when annotating images that should reflect a wide range of perspectives, such as facial recognition datasets. By sourcing annotations from a global crowd, you can ensure that your data is representative of the real-world diversity found in your target application. This enhances the quality and fairness of machine learning models.

Quality Control

While humans can provide high-quality annotations, maintaining consistency and accuracy can be challenging when annotators are overwhelmed with a high volume of images. Crowdsourcing addresses this issue by distributing the workload among a larger number of annotators. This not only speeds up the process but also allows you to maintain quality standards by cross-verifying annotations. Crowdsourcing platforms often incorporate quality control mechanisms, such as redundancy and consensus, to ensure the accuracy of annotations.

Specialized Expertise

Depending on the nature of your image annotation task, crowdsourcing can provide access to specialized expertise. For instance, if you require annotations in a niche field like medical imaging or art classification, you can find workers with domain knowledge who can provide more accurate and relevant annotations. This ensures that the annotations are of the highest quality and align with the specific requirements of your project.

Versatility

Crowdsourcing can be applied to various image annotation tasks, including object detection, image segmentation, text recognition, and more. It can also adapt to different data formats, such as images, videos, or 3D scans, making it a versatile solution for various machine learning and computer vision applications.

Crowdsourcing image annotation offers a cost-effective, efficient, and scalable solution for labeling and annotating large image datasets. It leverages the power of a diverse crowd of remote workers to ensure quality, accuracy, and representation of various perspectives in your annotated data. Whether you're a startup looking to build a machine learning model or a large corporation with extensive image data needs, crowdsourcing is a valuable technique to consider for image annotation tasks.

4. Best Practices for Image Annotation

I. data quality and accuracy.

To prioritize data quality and accuracy in image annotation, it's essential to employ expert annotators, establish clear annotation guidelines, and maintain an iterative review process. Feedback and communication with annotators are crucial, and the use of quality assurance tools can help detect and rectify errors.

Small pilot datasets can be used for validation before scaling up, and continuous training keeps annotators updated. Emphasizing quality over quantity is vital, as a smaller dataset with meticulously labeled data points ensures better model performance by preventing anomalies and mislabeled instances from affecting results.

II. Consistency and Standardization

Consistency and standardization play a crucial role in image annotation, ensuring the accuracy of machine learning models. Objects of interest often have varying degrees of sensitivity, demanding uniformity in the annotation process. For example, when labeling a "crack" on a vehicle body part, consistent criteria are essential across all images.

Precise annotation guidelines, constant inter-annotator communication, and annotation tools help maintain this uniformity, minimizing the risk of model confusion due to ambiguous or inconsistent annotations. To maintain consistency and standardization, regular quality checks, standardized nomenclature, and an iterative approach for process improvement are key.

These practices create a reliable and standardized dataset, enabling machine learning models to learn and make accurate predictions based on a consistent understanding of labeled objects and attributes. Inconsistencies in annotation can lead to reduced model accuracy, making it imperative to emphasize and implement these best practices in the annotation process.

III. Data Security and Privacy

In the realm of image annotation, safeguarding data security and privacy is paramount to protecting individuals' rights and ensuring the ethical use of personal information. Data security involves implementing robust measures to protect electronic data from unauthorized access, which is especially crucial when outsourcing data annotation projects.

The ever-increasing adoption of AI and machine learning technologies has made data security and privacy compliance issues more pressing. Training data often contains sensitive personal information, such as names, addresses, and birthdates. Inadequate security measures can result in data breaches, potentially leading to identity theft and malicious use.

When considering outsourcing annotation, it's essential to assess vendors' data security protocols to safeguard your data throughout the process, from ingestion to delivery.

Crowdsourcing is a popular method for obtaining training data quickly and cost-effectively. However, it carries substantial risks, including quality control issues, security vulnerabilities, and potential cost overruns. The lack of control over annotators' qualifications and the absence of confidentiality guarantees pose significant security concerns.

When selecting a data annotation provider, it's vital to choose one with stringent security measures and high-quality standards to protect your data. Moreover, the careful handling of personal information during the annotation process is crucial to maintaining trust.

Companies specialize in data annotation for large training sets, embedding layers of security into their processes, including physical, internal, and cybersecurity measures to ensure data privacy and security compliance. These security considerations are essential to the responsible and ethical use of personal data in the field of image annotation.

IV. Annotator Training and Guidelines

Annotator training and guidelines are fundamental to the success of image annotation projects. Once you have established your annotation schema, it's imperative to invest in training your data annotators on how to use it accurately and efficiently.

Various methods can be employed for this purpose, such as online courses, workshops, quizzes, feedback sessions, and mentorship. Providing a structured training program ensures that annotators understand the annotation guidelines and criteria thoroughly, reducing the risk of errors and inconsistencies.

It's also vital to continuously monitor annotators' progress and performance, offering regular feedback and support to address any issues or uncertainties that may arise during the annotation process. Encouraging open communication among annotators and with project managers is equally important, as it allows for the exchange of insights and the quick resolution of any questions or ambiguities.

Annotation Guidelines

Before, during, and after the annotation process, certain principles should guide your approach. Prior to commencing the collection of annotations, it's advisable to set a quality goal for your gold dataset.

This goal should be based on expert benchmarks or past annotation tasks, helping you define the level of quality needed for a successful project outcome. During the annotation process, adherence to established guidelines is crucial to maintain consistency and accuracy.

Regular quality checks and feedback loops should be implemented to identify and rectify any deviations from the guidelines promptly. Post-annotation, it's essential to continuously refine the guidelines based on lessons learned and feedback from annotators to improve the overall quality of the dataset.

Annotator training and guidelines serve as the foundation for the success of image annotation projects, ensuring the reliability and precision of the data generated.

5. Image Annotation Tools and Software

I. labellerr.

Labellerr is your go-to tool for high-quality image and video annotation, boasting advanced automation and smart quality assurance. With the capability to process millions of images and thousands of hours of videos in just a few weeks, Labellerr is a game-changer for AI teams.

Key features include:

  • Automated Labeling: Enjoy prompt-based, model-assisted, and active learning-based labeling automation for lightning-fast results.
  • Multiple Data Types Support: Whether it's images, videos, PDFs, text, or audio, Labellerr supports a wide range of data types for diverse project needs.
  • Smart QA: Our pre-trained model and ground-truth-based quality assurance ensures accurate annotations.
  • MLOps Integration: Seamlessly integrate Labellerr with your AI infrastructure, including GCP Vertex AI, AWS SageMaker, and custom environments.
  • Project Management and Advanced Analytics: Optimize your projects with prompt-based labeling, model-assisted labeling, and active learning, all supported by Labellerr's advanced analytics.
  • 24/7 Support: Get round-the-clock technical support for your critical projects with the fastest response times.

Labellerr's impact is backed by satisfied customers, such as Intuition Robotics, Wadhwani AI, Perceptly Inc., and more. With impressive metrics that matter, including 99% accurate labels and significant reductions in time to data preparation and development costs, Labellerr is a must-have tool for AI teams looking to accelerate their data preparation efforts.

Try a free 7-day pilot with no credit card required and experience the future of image annotation with Labellerr.

II. Labelbox

- Overview: Labelbox is a comprehensive image annotation platform that aims to simplify the process of labeling data for machine learning. It provides a cloud-based solution for annotation, enabling teams to collaborate on labeling tasks.

- Features: Labelbox supports a wide range of annotation types, including bounding boxes, polygons, segmentation masks, and key points. It also offers features for text annotation and classification. Its user-friendly interface makes it accessible to both technical and non-technical users.

- Scalability: Labelbox is designed for scalability and can handle large datasets. It offers automation and data management features to optimize labeling workflows.

- Integration: It provides integrations with popular machine learning frameworks and tools, allowing for seamless data preparation and model training.

- Use Cases: Labelbox is suitable for various use cases, including object detection, image segmentation, autonomous vehicles, medical imaging, and more.

III. Supervised

- Overview: Supervised is an annotation tool that leverages active learning to make the annotation process more efficient. It helps users label the most informative data points, reducing the overall labeling workload and cost.

- Active Learning: The tool uses machine learning models to select data points that are most uncertain, ensuring that annotators focus on the samples that will have the greatest impact on model performance.

- Integration: Supervised can be integrated into your machine learning workflow, ensuring that annotated data is immediately usable for training and evaluation.

- Cost-Efficiency: By reducing the number of annotations required to achieve a certain level of performance, Supervised can be a cost-effective solution for data labeling.

IV. VGG Image Annotator (VIA)

- Overview: VIA is an open-source image annotation tool developed by the Visual Geometry Group at the University of Oxford. It's a lightweight and user-friendly solution for basic annotation tasks.

- Simplicity: VIA is known for its simplicity and ease of use. It is designed for quick image annotation, making it suitable for small-scale projects and research purposes.

- Annotation Types: While it doesn't offer an extensive range of annotation types, VIA supports basic annotations like bounding boxes, polygons, and lines.

- Open Source: Being open source, VIA can be customized and extended to suit specific research needs, making it a valuable resource for the academic and research community.

- Overview: LabelMe is an online platform designed for image annotation and labeling. It is particularly popular among researchers and small-scale annotation projects.

- User-Friendly Interface: LabelMe provides a straightforward interface for drawing bounding boxes, polygons, and other annotations directly on images.

- Community Dataset: LabelMe offers an open-access dataset with annotated images, which is widely used in the computer vision research community for benchmarking and training machine learning models.

- Research Focus: LabelMe is commonly used for academic and research purposes, allowing users to contribute to and benefit from a shared dataset.

VI. COCO Annotator

- Overview: COCO Annotator is a specialized tool tailored for annotating data in the COCO format. COCO is a widely used dataset format for object detection and segmentation tasks.

- COCO Format Support: This tool streamlines the process of annotating images and their associated metadata according to the COCO format, making it a suitable choice for projects that require compatibility with the COCO dataset .

- Efficiency: It focuses on efficient annotation workflows specific to the COCO standard, allowing for the quick creation of annotated datasets.

- Use Cases: COCO Annotator is ideal for projects in computer vision that utilize the COCO dataset structure, including object detection, keypoint detection, and segmentation.

VII. Custom In-House Tools

- Overview: Custom in-house tools are annotation software developed internally by organizations to meet specific project requirements.

- Tailored Solutions: These tools are designed to be highly customized, allowing organizations to cater to their unique annotation needs and data formats.

- Control and Flexibility: In-house tools provide complete control over the annotation process, which can be essential for projects with specific data requirements or constraints.

- Development and Maintenance: While powerful, creating and maintaining custom tools can be resource-intensive, which may not be cost-effective for smaller projects. Organizations should weigh the benefits against the development effort.

Each of these tools has its strengths and may be better suited to different use cases and project requirements. The choice of tool ultimately depends on factors such as the complexity of the annotation task, project scale, available resources, and the desired level of customization and control.

6. Challenges and Pitfalls

Image annotation is a crucial step in training machine learning models, particularly in computer vision tasks. However, it comes with several challenges and pitfalls that need to be carefully addressed. Here are some of the key challenges and pitfalls associated with image annotation:

I. Ambiguity and Subjectivity

- Images can be ambiguous, making it challenging to determine the correct annotation. Annotators may interpret images differently, leading to subjectivity in the annotations. For example, the classification of objects in partially occluded or low-quality images may vary among annotators.

- Mitigation: Clear annotation guidelines and constant communication with annotators can help reduce ambiguity and subjectivity. Training annotators to handle specific edge cases and providing them with reference examples can also improve consistency.

II. Scale and Cost

- Annotating a large dataset with thousands or millions of images can be costly and time-consuming. The cost of hiring annotators, acquiring equipment, and managing the annotation process can add up quickly.

- Mitigation: To reduce costs, you can consider using pre-annotated datasets , using crowdsourcing platforms , or leveraging automated annotation tools. Prioritizing data selection and focusing on the most relevant images for your task can also help manage costs.

III. Time-Consuming Nature

- Image annotation can be time-consuming, especially when dealing with complex or detailed annotations like object segmentation. This can slow down the overall development of machine-learning models.

- Mitigation: Streamlining the annotation process with well-defined workflows, using specialized annotation software, or outsourcing to professional annotators can help save time. Automated annotation tools and techniques , such as weak supervision , can also expedite the process.

IV. Quality Assurance

- Ensuring the accuracy and consistency of annotations across a large dataset can be challenging. Annotators may make mistakes or drift in their understanding of the annotation guidelines over time.

- Mitigation: Implementing a robust quality assurance process is essential. This may involve having multiple annotators review and cross-check annotations, conducting periodic meetings and training, and using automated tools to flag potential issues. Regularly monitoring and providing feedback to annotators can help maintain annotation quality.

In addition to these challenges, it's important to consider data privacy and security when sharing and storing annotated images, especially when dealing with sensitive or personal data. Also, staying updated with best practices in image annotation and machine learning techniques can help address evolving challenges in this field.

Overall, addressing the challenges and pitfalls of image annotation requires a combination of careful planning, clear communication, quality control measures, and the use of appropriate tools and techniques to streamline the process.

7. Industry Applications of Image Annotation

I. autonomous vehicles.

- Object Detection and Recognition: Image annotation is crucial for autonomous vehicles to detect and recognize objects on the road, such as other vehicles, pedestrians, traffic signs, and obstacles. This is essential for making real-time driving decisions.

- Lane and Path Marking: Annotated images help in identifying road lanes and path markings, enabling the vehicle to stay within its lane and navigate safely.

Autonomous Vehicles Image Annotation

II. Healthcare and Medical Imaging

- Disease Diagnosis: In medical imaging , image annotation is used for annotating various anatomical structures and abnormalities, aiding in the diagnosis of diseases like cancer, heart conditions, and neurological disorders.

- Radiology and Pathology: Radiologists and pathologists rely on annotated medical images for accurate assessments and treatment planning.

- Tracking Progress: Annotated images are valuable for tracking disease progression and the effectiveness of treatments over time.

III. E-commerce and Retail

- Product Recognition: Image annotation is used for recognizing and categorizing products. This is helpful for automated inventory management , product recommendations, and visual product searches .

- Visual Search: Annotated images enable visual search capabilities, allowing customers to search for products using images rather than text queries.

- Quality Control: Image annotation is utilized to identify defects or irregularities in products during the manufacturing or quality control process.

IV. Agriculture

- Crop Monitoring: Image annotation in agriculture involves the labeling of crops, pests, and diseases, facilitating the monitoring and management of crop health .

Crop Monitoring

- Precision Farming: Annotated aerial images aid in precision farming, enabling farmers to optimize the use of resources like water, fertilizer, and pesticides.

- Harvesting and Yield Prediction: Annotated images help in predicting crop yields and optimizing the harvesting process.

Agriculture Annotation

V. Geospatial Analysis

- Land Cover Classification: Image annotation is used to classify land cover types, such as forests, urban areas, and water bodies, which is valuable for land-use planning and environmental monitoring.

- Change Detection: Annotated images from different time periods allow for the detection of changes in landscapes, which is critical for urban development, disaster response, and environmental studies.

- Infrastructure Planning: Geospatial annotation aids in the planning and maintenance of infrastructure, including roads, bridges, and utilities.

Geospatial Analysis

VI. Security and Surveillance

- Object Detection: Image annotation helps in identifying and tracking objects or individuals of interest in security and surveillance footage.

- Anomaly Detection: Annotated images are used for detecting unusual or suspicious activities in crowded places, critical infrastructure, and public spaces.

- Forensics: In criminal investigations, image annotation supports the analysis of evidence, including fingerprints , facial recognition , and other forensic tasks.

Image annotation plays a pivotal role in these industries, enhancing the capabilities of AI and machine learning systems. Accurate and well-labeled images enable these sectors to make informed decisions, automate processes, improve safety, and enhance overall efficiency.

Security and Surveillance

As the technology evolves, image annotation will continue to be an integral part of various applications across industries, driving advancements in AI and data-driven decision-making.

8. Regulations and Ethical Considerations in Image Annotation

Image annotation plays a crucial role in various fields, including computer vision, machine learning, and artificial intelligence. However, it is essential to ensure that image annotation processes adhere to regulations and ethical considerations to protect individual rights, maintain fairness, and uphold ethical standards. This article focuses on three primary aspects: GDPR and data privacy, bias and fairness, and ethical guidelines.

I. GDPR and Data Privacy

The General Data Protection Regulation (GDPR) is a comprehensive data protection law in the European Union that has significant implications for image annotation. GDPR is designed to protect the personal data of EU citizens, and this includes any images that may contain identifiable individuals. When annotating images, organizations must comply with GDPR by obtaining informed consent from individuals whose images are being used for annotation. Key considerations related to GDPR and image annotation include:

a. Consent: Image annotation projects must ensure that individuals depicted in images have given their explicit consent for the use of their images for annotation purposes. Consent forms should clearly explain how the data will be used and provide individuals with the option to withdraw their consent at any time.

b. Anonymization : Personal data, such as faces and other identifiable features, should be appropriately anonymized to prevent the identification of individuals in annotated images.

c. Data Security: Organizations should implement robust data security measures to protect annotated images from unauthorized access, breaches, or misuse.

d. Data Retention : Images used for annotation should not be retained longer than necessary, and individuals have the right to request the deletion of their data.

II. Bias and Fairness

Bias in image annotation can lead to unfair and discriminatory outcomes in AI applications. It is crucial to address bias and promote fairness throughout the image annotation process. This includes:

a. Diversity and Representation: Image annotation teams should be diverse and inclusive to ensure a broad perspective during the annotation process. This can help mitigate potential bias.

b. Bias Detection : Implement tools and methodologies to detect and mitigate biases in annotated data. Review and audit annotations to identify and rectify bias-related issues.

c. Guidelines and Training: Provide clear guidelines to annotators about avoiding stereotypes, harmful stereotypes, and favoring fairness. Training annotators on the ethical aspects of image annotation is crucial.

d. Fairness Metrics: Establish fairness metrics and benchmarks to measure and report on potential biases in AI models developed using annotated data.

III. Ethical Guidelines

Ethical considerations in image annotation go beyond legal compliance and fairness. Ethical guidelines help ensure that the annotation process respects human dignity and values. Some important ethical considerations include:

a. Respect for Human Rights: Annotation projects should not compromise individual rights, and images should be annotated in a way that respects privacy and dignity.

b. Ethical Review: Establish an ethical review process for image annotation projects, involving experts who can assess the potential impact on individuals and society.

c. Transparency: Ensure transparency in the annotation process , including disclosing the purpose of annotation and its potential consequences.

d. Accountability: Assign responsibility for ethical oversight and adherence to guidelines within organizations involved in image annotation.

Image annotation is a critical step in developing AI applications, but it must be conducted with careful attention to regulations and ethical considerations. GDPR compliance, addressing bias and fairness, and adhering to ethical guidelines are essential to ensure the responsible and ethical use of annotated image data.

9. Future Trends in Image Annotation

I. integration of ai in annotation.

As the field of image annotation advances, the integration of AI technologies is poised to play a pivotal role in shaping the future. Here's a detailed look at this trend:

- Automated Annotation: AI-powered annotation tools are becoming increasingly sophisticated, capable of automating the labeling process to a significant extent. This includes techniques like image segmentation, where AI algorithms can identify objects or regions of interest in images. As AI models improve, they can provide preliminary annotations that human annotators can review and refine, greatly reducing the manual effort required.

- Active Learning: AI is being used to improve the efficiency of annotation by selecting the most informative samples for human annotation. Active learning algorithms can identify data points that are more challenging or uncertain for the model, thereby prioritizing the labeling of these samples. This reduces the overall annotation workload and can lead to more accurate models with fewer labeled examples.

- Transfer Learning: AI models trained on large and diverse datasets can be leveraged for image annotation tasks. Pre-trained models, such as those for object detection, segmentation , and facial recognition, can be fine-tuned for specific annotation needs. This transfer learning approach accelerates the annotation process by capitalizing on existing knowledge within the models.

- Quality Assurance: AI is being integrated into the annotation pipeline to ensure the quality of annotations . Algorithms can flag and review potentially inaccurate or inconsistent annotations, leading to improved data quality and reducing the need for manual error correction.

- Semantic Annotation: AI is helping in adding more meaning to annotations. For example, in addition to labeling an object as "car," AI can provide additional attributes, such as color, make, and model. This richer semantic annotation enables more nuanced and context-aware machine learning models .

II. Real-Time Annotation

Real-time annotation is another emerging trend in image annotation, offering several advantages:

- Live Data Labeling: Real-time annotation involves annotating data as it is being generated or captured. This is particularly relevant in applications like autonomous vehicles, where camera feeds are continuously generated. Real-time annotation allows for immediate feedback and model adaptation based on the most recent data.

- Remote Collaboration: Real-time annotation tools facilitate remote collaboration among annotators and experts. Geographically dispersed teams can collectively label data simultaneously, reducing annotation times and enabling real-time decision-making.

- Training Data Refresh: In applications like object detection, models require consistent and up-to-date training data to adapt to changing conditions. Real-time annotation ensures that models are trained on the most current and relevant data, which is critical for tasks like surveillance and security.

- Quality Control: Real-time annotation enables immediate quality control. Annotators can review and verify annotations as they are made, addressing any issues or uncertainties on the spot, which contributes to higher-quality training data.

- Emergency Response: In scenarios where real-time decisions are critical, such as emergency response or disaster management, real-time annotation can provide vital information for AI systems to aid in decision-making and response coordination.

The adoption of real-time annotation are at the forefront of image annotation trends.  Real-time annotation is becoming increasingly relevant in applications that require instant decision-making and adaptability based on the most current data.

III. Annotating 3D Data

Understanding 3D Annotation

In the rapidly evolving landscape of image annotation, one of the most promising future trends is the annotation of 3D data. While traditional 2D annotations have been fundamental for various applications, annotating 3D data opens up new dimensions, literally and figuratively. This article explores the concept of annotating 3D data and its potential impact on diverse industries.

3D annotation involves the process of adding annotations and labels to three-dimensional data. It's particularly relevant when dealing with point clouds, which represent objects or scenes in three dimensions. This approach takes into account depth, distance, and volume, providing a richer source of information.

3D annotation is often performed using cuboids, but it can also involve the use of voxels, which are 3D pixels used for semantic segmentation.

Annotation 3d

Applications of 3D Annotation

The applications of 3D annotation are broad and diverse. It's especially valuable in scenarios where 2D visual data falls short. Here are some key industries and applications where 3D annotation can make a significant difference:

1. Medical Imaging

- 3D annotation is essential for analyzing medical scans like CTs and MRIs. It enables precise identification and segmentation of anatomical structures, tumors, and abnormalities in a three-dimensional space.

2. Geospatial Analysis

- In geospatial applications, 3D annotation helps in detecting and understanding three-dimensional structures, such as buildings or topographical features, from data sources like synthetic-aperture radar imagery.

3. Automotive and Autonomous Driving

- The automotive industry relies on LiDAR point cloud data for autonomous vehicles. 3D annotation plays a crucial role in tracking objects, pedestrians, and other vehicles in a three-dimensional environment.

4. Industrial Inspection

- Quality control and anomaly detection in industrial settings benefit from 3D scans of products. These scans allow for the identification of defects, measurements, and structural analysis.

5. Agriculture

- 3D annotation can empower harvesting robots by enabling them to accurately identify and pick fruits based on their three-dimensional characteristics.

6. Retail and Augmented Reality (AR)

- Retail applications leverage 3D annotation for detecting user gestures and poses, enhancing virtual and augmented reality experiences.

Pros and Cons of 3D Annotation

As with any technology, 3D annotation comes with its set of advantages and challenges:

1. More Informative

- 3D annotation takes into account additional dimensions, offering richer and more useful information. This is particularly valuable in adverse conditions like fog, darkness, and occlusion, where 2D data might be limited.

2. 2D Approximation

- It's possible to perform 3D annotations with reasonable approximations on 2D data. For instance, segmentation of sequences of DICOM slices or estimating cuboids in flat images based on perspective.

1. Complex and Time-Consuming

- 3D annotation can be a time-consuming process, requiring advanced tools capable of handling large datasets.

2. Data Quality

- The quality of 3D annotations can be affected by the dispersion of LiDAR data points as objects move further away from the source.

Tips for Effective 3D Annotation

To maximize the effectiveness of 3D annotation, consider the following tips:

1. Visualization Tools

- Use tools that allow you to visualize multiple viewpoints (side, top, front) to simplify annotating a single object. Comparing 3D data to a 2D capture of the same scene can serve as a valuable sanity check.

2. Medical Imaging Advantages

- Take advantage of smart segmentation based on tissue density for precise annotations in medical imaging.

3. Consistent Orientation

- Maintain a consistent position along the pitch axis when creating 3D annotations, which eases the labeling of the roll and yaw axes.

The future of image annotation is increasingly tied to the ability to annotate three-dimensional data. As technology and tools for 3D annotation advance, industries such as healthcare, geospatial analysis, automotive, and more will experience significant improvements in data quality and insights.

As the world becomes more reliant on three-dimensional data sources, mastering 3D annotation will be crucial for enhancing the capabilities of machine learning models and computer vision applications.

III. Enhanced Data Augmentation

The Importance of Data Augmentation Data augmentation has been a standard practice in the deep learning community for years. It involves applying various transformations to the existing dataset to generate new examples. These transformations may include rotations, flips, zooms, and color adjustments. The primary goal of data augmentation is to improve model generalization by exposing it to a more extensive range of variations in the data.

Challenges with Traditional Data Augmentation While traditional data augmentation techniques have proven effective, they have limitations. They often rely on basic geometric and pixel-level manipulations. This approach may not capture the full spectrum of real-world variations that images can exhibit. In many cases, the diversity introduced by traditional augmentation may be insufficient for training highly robust models.

Enhanced Data Augmentation Techniques The future of image annotation is shifting towards enhanced data augmentation techniques that provide a more comprehensive and nuanced understanding of the data. These techniques are designed to replicate complex real-world scenarios and challenges. Some of the exciting trends in enhanced data augmentation include:

  • Generative Adversarial Networks (GANs)

GANs are being used to generate synthetic data that is indistinguishable from real data. This approach can significantly increase the diversity of the training dataset. For image annotation, GAN-generated images can provide valuable annotations that might be challenging to collect manually.

- Simulated Environments

Simulated environments, such as video games or 3D modeling, are increasingly used for data augmentation. These environments allow the creation of highly realistic scenes and objects. Models trained on data from simulated environments can exhibit better generalization when applied to the real world.

  • Domain-specific Augmentations

Enhanced data augmentation techniques are tailored to specific domains. For example, medical imaging may benefit from anatomically accurate deformations, while autonomous driving datasets may include simulations of adverse weather conditions and challenging road scenarios.

Challenges and Considerations While enhanced data augmentation holds great promise, it comes with its challenges: (i) Computational Resources

GANs and simulations can be computationally intensive. Training and generating synthetic data may require significant resources.

(ii) Data Privacy

GANs raise concerns about privacy, as they can generate highly realistic faces, objects, or scenes. Striking the right balance between realism and privacy is crucial. (iii) Validation and Evaluation

Assessing the quality and realism of augmented data is an ongoing challenge. Metrics and evaluation techniques for synthetic data are still evolving.

Enhanced data augmentation is at the forefront of future trends in image annotation. As the field of machine learning continues to advance, the ability to generate diverse, realistic, and domain-specific data will be a game-changer for training deep learning models.

The integration of GANs, simulated environments, and domain-specific augmentations promises to elevate the accuracy and robustness of image annotation, making it a key enabler for numerous applications in computer vision and beyond.

10. Case Studies

I. enhancing autonomous vehicle safety with image annotation.

Client: Airbus Autonomous Vehicles

Airbus Autonomous Vehicles, a leading company in self-driving car technology, aimed to improve the safety and reliability of their autonomous vehicles. They needed to annotate vast amounts of real-world driving scene images to train their vehicle's computer vision systems.

To ensure the safe operation of autonomous vehicles, Airbus needed high-quality image annotations, including the identification of various objects on the road, such as pedestrians, vehicles, road signs, and lane markings. This required precise and consistent annotations to enhance the vehicle's decision-making algorithms.

Airbus partnered with an image annotation service provider, AnnoTech, to accomplish this task. AnnoTech deployed a team of skilled annotators who received comprehensive training on annotating images to industry standards.

They used advanced annotation tools that allowed them to label objects with accuracy, ensuring that each object's boundaries were clearly defined and properly categorized.

With the help of AnnoTech's image annotation services, Airbus successfully annotated thousands of images to train their autonomous vehicles. The annotated data significantly improved the vehicles' ability to recognize and respond to objects and scenarios on the road.

This led to increased safety, better decision-making, and enhanced autonomous driving capabilities. The accuracy of object recognition and classification improved from 75% to 95%, reducing the risk of accidents and improving the overall performance of their autonomous vehicles.

The success of this image annotation project demonstrated the critical role of high-quality data in the development of self-driving technology, making autonomous vehicles safer and more reliable for the general public.

II. Enhancing Recycling Efforts with Image Segmentation

Client: Waste Management & Recycling Company in Belgium

Waste Management

A waste management and recycling company in Belgium aimed to improve its recycling efforts and ensure the proper disposal of waste materials by implementing an AI solution that could identify and classify the type of waste in the containers of businesses.

They required image annotation services for their computer vision system to recognize various waste categories, including paper, plastic, wood, garbage bags, glass, food, and dangerous waste. Challenges

The client faced the challenge of accurately annotating images of waste materials using polygon annotations to categorize and define different types of trash. Image segmentation requires a high level of precision and attention to detail to train the AI system effectively. Solution

The client chose to collaborate with Mindy Support, an experienced data annotation service provider, based on recommendations from a long-standing partner and a previous successful project.

Mindy Support assembled a team of skilled data annotators who were already proficient in data labeling projects. They meticulously labeled the waste materials in the images, achieving a remarkable quality score of 98% during the annotation phase, surpassing the client's expectations by 3%.

Through the partnership with Mindy Support, the client not only achieved a quality score of 98% but also maintained a successful collaboration for over two years. The accurate image annotations enabled the implementation of their AI solution, which contributed to tracking and improving recycling efforts, ensuring the proper sorting of waste, and enhancing the overall quality of recycled materials.

As a result, the client was highly satisfied with the project's outcomes and considered expanding the taxonomy to include more waste categories, aligning with the evolving needs of recycling technology.

This case study illustrates how image annotation services can play a crucial role in waste management and recycling, enhancing efficiency, safety, and sustainability in the industry.

III. Enhancing Construction Progress Monitoring with Image Annotation

Client: Construction Progress Monitoring Product Developer

Construction Progress case

A construction progress monitoring product developer created a system designed to monitor and evaluate the advancement of construction projects over time. To enhance the accuracy of their product, they required annotated image datasets featuring construction sites at different points in time. These annotated datasets were essential for training their machine learning algorithms to compare images and assess the progress made on construction sites. Challenge

The client faced the challenge of annotating a substantial volume of image data, including images of construction sites from multiple years. Both current images and historical images were necessary to enable the system to compare and measure progress accurately. The annotation task involved identifying and labeling various elements within the images, such as construction structures, parking areas, paths, roads, stadiums, and different types of surfaces (paved and unpaved).

The client formed a dedicated team of 10 full-time data annotators for the project. The client specifically requested the use of QGIS, a free and open-source geospatial data annotation tool, to perform the image annotation tasks. The output format required was GeoJSON, commonly used for encoding geographic data structures. The team of annotators meticulously labeled and annotated the various elements within the images, ensuring that the construction progress monitoring system could accurately compare and evaluate changes in construction sites over time. Approximately 1,000 total hours were devoted to annotating the required volume of data within the specified project timeframe.

Case Study Construction

By collaborating, the client successfully obtained accurately annotated image datasets. These datasets empowered their construction progress monitoring product to conduct precise comparisons between images of construction sites from different time periods.

The annotated data significantly improved the system's capability to measure and assess progress on construction sites accurately. This case study underscores the critical role of image annotation in the construction progress monitoring industry, enabling more precise and efficient tracking and evaluation of construction site developments over time, ultimately enhancing the success of construction projects.

In the realm of artificial intelligence and machine learning, image annotation plays a central and indispensable role. We have explored several image annotation tools and software that cater to a diverse array of projects and requirements. These tools, such as Labelbox, Supervised, VIA, LabelMe, COCO Annotator, and custom in-house solutions, offer a spectrum of features and functionalities to aid in the process of data labeling.

However, the importance of image annotation goes far beyond the tools themselves, extending to its role in shaping the future of AI and the ethical and accurate handling of data.

The central role of image annotation in AI

Image annotation is the backbone of AI, enabling machines to understand and interpret the visual world. Through methods such as object detection, image segmentation, and keypoint annotation, image data is transformed into labeled information, allowing machine learning models to recognize patterns, make predictions, and perform tasks with human-like comprehension.

Whether it's autonomous vehicles recognizing road signs, medical AI systems diagnosing diseases from medical images, or any application where visual data is involved, accurate annotation is essential for training reliable and effective AI models.

The future of image annotation

The future of image annotation is marked by ongoing advancements in automation and efficiency. Tools like Supervised, with their active learning capabilities, aim to reduce the annotation workload and cost while maintaining high-quality labeled data. Integrations with machine learning frameworks and AI pipelines are becoming more seamless, making it easier to put annotated data to practical use. In addition, the development of new annotation techniques for emerging technologies, such as augmented reality and autonomous robotics, is on the horizon. As AI continues to evolve, so too will image annotation methods to support its growth.

Importance of ethical and accurate annotation

Ethical and accurate image annotation is of paramount importance. As AI models increasingly influence decision-making processes in various domains, the data used to train these models must be free from bias and errors.

Ethical considerations encompass issues like fairness, transparency, and privacy in data labeling. For example, ensuring that AI systems do not perpetuate societal biases or violate privacy rights is a critical aspect of annotation ethics.

Moreover, annotation accuracy is crucial to the success of AI applications. Annotators must adhere to high standards to create ground truth data that allows AI models to operate with precision and reliability. Errors in annotation can lead to incorrect model predictions, impacting real-world applications such as healthcare, autonomous transportation, and national security.

In conclusion, image annotation is not just a technical step in the AI pipeline; it's a fundamental driver of AI's capabilities and ethical responsibilities. As image annotation tools continue to evolve and address the diverse needs of the AI community, the future promises more efficient workflows and new possibilities for AI applications.

It is imperative that we approach image annotation with a commitment to both accuracy and ethical integrity to ensure that the AI systems we develop are fair, reliable, and beneficial to society as a whole.

Frequently Asked Questions

1. Which data annotation tools should you use?

When it comes to data annotation, you have the flexibility to choose from commercial, open-source, or free annotation tools. These tools are designed to cater to different needs and come with a range of feature sets, providing diverse capabilities. They enable your workforce to efficiently annotate various data types, including streams, frames, and images, whether they are single-frame images, multi-frame sequences, or even videos.

This versatility ensures that you can select the most suitable tool for your specific annotation requirements, allowing you to streamline the annotation process and enhance the quality of your labeled data .

2. Why is image annotation important in computer vision?

Image annotation establishes the benchmarks that neural models strive to replicate, meaning any inaccuracies in the labels can be perpetuated. As a result, the accuracy of image annotation serves as the cornerstone for training neural networks, making annotation a critical and fundamental task in the field of computer vision .

3. How can a medical image be annotated?

Medical images can be enhanced for the detection of abnormalities by adding labels that outline the boundaries of cells or structures within the image. To perform this task, data annotation tools have become increasingly popular and essential, as they enable the application of these annotations to your medical image data .

These tools facilitate the process of marking and highlighting specific regions of interest in the images, which is crucial for various medical applications such as disease diagnosis, treatment planning, and research. By using these annotation tools, healthcare professionals and researchers can more accurately analyze and interpret medical images, ultimately improving patient care and advancing medical knowledge.

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Best Online Annotation Tools for Client Feedback

Annotation tools empower project management and team collaboration, but it can take time to find the right one. read about the top 15 tools.

author profile image

Merrin Hughes

May 12, 2023

Best Online Annotation Tools for Client Feedback||

If you work in project management, quality assurance, or any field where feedback is essential to day-to-day operations, it’s time to consider using an annotation tool.

Annotation tools are software that teams use to simplify their communications and workflow — as well as to facilitate seamless collaboration across multiple projects. Use them to attach text or visual notes to content, aggregate and organize information, and track quality control issues in real time.

With so many annotation tools on the market, it can be challenging to find the right one. Video annotation tools, image annotation tools, data annotation tools - the choice can be overwhelming. Check out our guide below to discover the best annotation software for your next project.

  • 1. 3 Things To Consider When Choosing An Annotation Tool
  • 2. Top 15 Online Annotation Tools in 2023
  • 2.1 BugHerd
  • 2.2 Hypothes.is
  • 2.3 Microsoft Office Lens
  • 2.7 Markup Hero
  • 2.8 Annotate
  • 2.10 Page Marker
  • 2.11 Drawboard
  • 2.12 Red Pen
  • 2.13 Go Visually
  • 2.14 Wipster
  • 2.15 Doccano
  • 3. Online Annotation FAQs

3 Things To Consider When Choosing Your Data Annotation Tool

Keep your search simple by comparing annotation tools and their key features based on three metrics: functionality, quality and efficiency, and price.

How you apply these metrics will depend on your industry and project type.  

Functionality

First, think about what annotation-related tasks you and your team need to complete on a regular basis. What are the key features you need? What will you be annotating? Consider if you need an image annotation tool, or a video annotation tool? Are you after something that will help with document annotation, or content screen annotation? These considerations will help you decide what the best annotation tool or annotation software is for you .

Choose an annotation tool with features that make it easier — not more challenging — to accomplish those tasks. Many online annotation tools offer an abundance of features, but not all will be relevant to your project.

For example, some annotation tools are so complex that they’re not recommended for beginners. Seek out a user-friendly tool if you’re gathering feedback from clients who not so technically savvy.

Projects that involve cross-team collaboration, likewise, need an annotation tool built with features that support real-time communication.

Quality and Efficiency

Prioritize annotation tools that have an established reputation for quality and efficiency.

Consider the app’s versatility, uptime, customer support resources, and any other aspects that will be relevant to your project.

Depending on your needs, here are a few other quality concerns worth thinking about:

  • Can you use the app to annotate documents, such as PDFs?
  • Does the software require local installation or is it cloud-based ?
  • Are learning resources, tutorials, or knowledge bases included with the tool?

This is where it helps to check out user reviews and demo videos.

If you’re on the fence about an annotation tool that’s free to use, there’s no reason not to test it out. Even paid tools offer trial periods so that you have a chance to assess the product’s features and functionality.

When it comes to annotation tools that aren’t free, confirm that the price is within your budget. Find out if you can buy the tool for a one-time payment or if it requires a subscription.

Review the pricing against the product’s functionality, quality, and efficiency, as discussed above. Who knows — in the course of your research, you might even discover a free annotation tool with more (or better) features.

Although paid tools can offer more sophisticated features, those features may not be necessary for your specific project.

Related: A Critical Comparison of the Best of the Best Website Feedback Tools

functionality, quality and efficiency

Top 15 Online Annotation Tools in 2023

Read our curated list of the best online annotation tools to save yourself the time and hassle of researching on your own.

BugHerd is an online visual feedback and bug-tracking tool that makes the work of annotating websites effortless. Recently BugHerd has also introduced some new features that allow for image annotation and document annotation (specifically markup tools for PDF annotation). It also includes design annotation tools that allow for annotations on Figma designs and prototypes.

BugHerd is the one annotation tool you need for the entire process of website design and build - making collaborative annotation simple.

Its user-friendly interface makes it simple for anyone to annotate and highlight information, relay feedback via screen captures, and (thanks to its handy automation features) instantly submit detailed bug reports.

You’ll need a BugHerd account to get started. Install the extension on your browser, then visit any web page you’d like to annotate. Use the BugHerd extension to snap a screenshot or screen recording before annotating them.

BugHerd is intuitive and easy to use. Developers and QA teams, in particular, will appreciate the product’s semi-automatic annotation for improved bug-reporting assistance, which automatically attaches information about the user’s operating system and browser with every bug report.

BugHerd also integrates with popular project management tools and collaboration software such as Asana , Jira and Slack.

BugHerd Online Annotation Features

  • Screen capture and recording: Take a screenshot or video of a web page, then attach comments.
  • Online file storage and management: Organize and manage your annotations in one place.
  • Issue management: Bug reports and annotated images are saved and sent to a Task Board for review.
  • Collaboration: Real-time commenting and notifications for resolved issue reports. BugHerd allows for collaborative annotation too, with the ability to have public and private annotations visible to your team and clients.
  • Incredibly simple to set up and use
  • Users can invite feedback from as many clients and stakeholders as they like
  • Team members can access their tasks and comments on a customized dashboard
  • Provide feedback using either a computer, a tablet, or a mobile phone
  • Annotations on websites and other digital documents, including image annotations and video annotation on embedded video.
  • Supports a limited number of third-party integrations
  • You’ll need to use Zapier or create a custom API if you’d like to integrate BugHerd with apps that are not currently supported

BugHerd offers a 14-day free trial for new users. A premium plan for five users costs $39 per month.

Hypothes.is

Hypothesis is a full-featured annotation tool designed with teachers, academics, editors, publishers, and journalists in mind. Its powerful suite of collaboration features supports real-time interaction between users.

With Hypothesis , teachers and students can collaborate on projects, share files, and add comments to facilitate classroom discussions and learning.

The tool also comes with research and fact-checking features. Users can verify information in real time without exiting the app.

  • Collaboration: Invite stakeholders to create a document as a team.
  • Fact-checking:   Cross-check the accuracy of the information and add sources.
  • Screen capture and annotation: Capture and add comments to screenshots.
  • Free to use
  • Simple user interface
  • Supports team collaboration
  • Allows content customization
  • Includes an extension for the Google Chrome toolbar
  • No issue-management features
  • Not suitable for web development teams
  • Limited integration with other business tools

Hypothesis is free to use. Sign up and install the tool’s Google Chrome extension to start collaborating.

Check Out: 17 Time Saving Tools and Templates for eLearning Designers

Microsoft Office Lens

Microsoft Office Lens is an optical character recognition (OCR) tool for collecting information from several web sources: photos, documents, notes, whiteboards, business cards, and much more.

Office Lens facilitates information exchange across systems by enabling users to scan and produce digital copies of any printed or written content.

The app is available on Android and iOS devices. It doesn’t require registration and it is free to use. Once installed, you can scan documents, extract texts from scanned images, detect text language, and more.

  • Text extraction: Copy texts from scanned images and save them in Word.
  • Image conversion: Office Lens converts photos to PDF, Word, and PowerPoint files and saves them either on-site or in the cloud.
  • Image scan and edit: Capture text from course materials like handouts, notes, and whiteboards. Users can highlight, crop, adjust, or add filters to an image.
  • Automatic multi-language detection: Identify the language of a document with an Office lens.
  • Text to speech: Copy text from images and automatically read them using the text-to-speech feature.
  • User-friendly interface
  • Excellent for mobile users
  • Supports image conversion to several file types
  • Doesn’t support team collaboration
  • Ideal for mobile phones but not web browsers
  • As of December 2020, no longer supports image storage with Google Docs or Google Drive

Office Lens is a free tool. Download it from your mobile phone’s app store to get started.

Skitch is an Evernote app designed for Mac, iPad, and iPhone that lets you take screenshots of your screen and edit them for use.

Using the built-in editing tool, you can add comments and mark-up screenshots with arrows, messages, colors, and highlights.

Skitch is simple to use, and because it’s a member of the expanding family of

cross-platform Evernote apps, it can sync with all of your devices. Note that you’ll need to sign up for an Evernote account in order to use it.

  • Screenshots and annotations: Use Skitch to capture and annotate images from the web.
  • File storage: Skitch saves and syncs screenshots across all your devices connected to Evernote.
  • Simple to use
  • Supports bulk image-editing
  • Synchronizes annotated images with your Evernote Notebook
  • Free for Evernote Premium users
  • Opening a paid Evernote Premium account is compulsory to use Skitch
  • Not available for other operating systems

Skitch is a complementary free tool for active Evernote Premium users but offers in-app purchases.

Diigo is a full-feature web annotation tool. Use Diigo to annotate PDFs and web pages while browsing the internet.

  • Cloud storage: Your notes, links, and images are always available through Diigo’s cloud storage whenever you browse the internet.
  • Collaboration and sharing: Diigo’s file-sharing features allow you to collaborate with coworkers, students, or friends.
  • Screen capture and editing: Add sticky notes, highlight text, tag your favorite web pages, create document outlines, and more.
  • Supports collaboration and sharing
  • Simplifies the process of gathering and organizing resources and bookmarks on the web
  • Free version offers limited features

Diigo offers four price tiers: free, standard, professional, and business.

The base plan is free but has fewer features. A standard plan is $40 a year, while a professional plan is $59 annually and a business plan is $10-per-user every month.

Weava is an academic research workspace developed by the Hong Kong-based corporation of the same name. It allows users to collect, organize, and collaborate on research and academic studies.

  • Export files: Export files in four formats: MS Word (.docx), Microsoft Excel, Plain text (.txt), and CVS.
  • Highlight and filter text: Underline specific passages or sentences from a website or PDF using custom highlight colors. Depending on how a user organizes their highlighters, they can filter them to:
  • Focus on specific colors
  • Narrow a research focus based on definitions or multiple aspects
  • Note creation: Weava provides a simple word-processing tool for making new notes without leaving the extension if you don’t want to work offline.
  • Full suite of features
  • Excellent for academic research as well as web design research
  • Provides cloud storage
  • Lacks dynamic collaboration features

Weava comes in both free and paid versions. The free version offers 100MB of cloud storage, unlimited highlights, and unlimited notes. Whereas, the paid version, which costs $3.99 per month, offers unlimited storage and all free plan features.

Markup Hero

Markup Hero is a sophisticated highlighting and markup web annotation tool for project managers, product managers, academics, and writers. It is available for Windows, Linux, Chrome, and Mac devices.

Use Markup Hero to upload images and PDFs, capture scrolling websites, and pin annotations to uploaded documents.

The app is available as a browser extension. Install it to visit a website and then capture, annotate, and share pages.

Digital teams and online agencies use Markup Hero for internal QA and user acceptability testing.

Markup Hero provides several annotation features, including:

  • Full-screen website captures: Capture an entire web page in one click.
  • Multi-page markup: Edit and annotate any page in a PDF file.
  • Document editing: Use Markup Hero’s toolbox of highlighters, pens, blurs, and arrows to modify a document.
  • Linkable resources: Share your edited documents with anyone.
  • Edit and markup history: View your image editing and markup history.
  • Supports integrations with over 15 project management tools
  • Robust collaboration features
  • Unsuitable for mobile apps
  • Free to use, but only if you don’t need to upload your screenshots

Markup Hero’s free version allows you to capture, upload, and markup a limited number of images. The superhero plan costs $4 per month (paid annually). This plan is for professionals and teams who need access to the app’s full suite of features.

Annotate is a smart digital workspace for collaborative document development. The app makes it incredibly simple to create and publish new documents while collaborating with team members throughout the process.

Annotate provides semi-automatic document fact-checking. Users can fact-check a specific phrase, sentence, or word to know its meaning and origin.

  • Document collaboration: Create documents with team members, fact-check information, exchange ideas through the live chat, and assign comments.
  • File sharing: Export files to MS Word and PDF formats.
  • Cloud storage and synchronization: Access your documents on any logged device on the go.
  • Easy to use
  • Supports collaboration
  • Good user permission controls
  • Provides cloud storage and supports third-party cloud storage.
  • Not available for iOS or Android

Annotate offers a free version for individual use. Large teams and businesses that need more customization features will have to purchase a paid plan. Plans are priced by user count and features.

With its easy-to-use interface and powerful video recording and picture editing features, Snagit transforms video lessons, training materials, and office communications.

The app supports webcam recording, scrolling screen captures, annotations, text captures, and more. You can record screens of any movie or take a screenshot of any screen, then edit it with effects.

  • Scrolling screen capture: Take screenshots of an entire web page, from top to bottom, in one click.
  • File sharing: Generate shareable links for any document edited in Snagit.
  • Create a video with images: Record your screen while annotating and editing an image.
  • Cloud storage: Save your files to Snagit’s cloud library to allow access from any device.
  • Text extraction: Grab text from captured images.
  • Easy to install
  • Shortcuts for easy launch
  • Creates high-quality screen video, webcam, system audio, and mic audio files
  • Provides an extensive library of presets and templates
  • Supports GIF exports, which is best for panoramic screen captures
  • Provides image editing and styling options
  • Supports fast renders, as well as easy saving and sharing
  • Limited video editing options
  • Doesn’t support editing or cropping for imported GIFs
  • No option for image size compression
  • Not a collaboration tool
  • Training is required to use the app effectively

Snagit charges a one-time license fee of $62.99. Before you buy, start with the trial version to get a better handle on the app’s features.

You Might Like: Ways to Accelerate Review and Approval for Websites and Digital Assets

list of annotation tools

Page Marker

Page Marker is a simple web-based annotation tool for quickly screenshotting and annotating websites. It’s available as a Chrome browser extension and supports annotation only, making it perfect for people who want a simple and straightforward way to annotate.

Page Marker ’s unique shortcuts help users highlight pages faster: Shift+D for the marker, Shift+H for highlighter, Shift+E for rubber, Shift+T for text, etc.

  • Text highlighter: Use the highlighter tool to highlight relevant text.
  • Screen capture: Snap and edit screenshots of a web page.
  • Image eraser: Use the eraser tool to erase any part of your drawing.
  • Dedicated annotation tool
  • Doesn’t require a subscription
  • Doesn’t support collaboration
  • Can’t record videos or perform complex functions like other apps
  • No cloud storage provided

This app is free to use.

Drawboard has two primary tools: Drawboard PDF and Drawboard Projects.

One of the most appealing features of Drawboard PDF is its drawing tools, which are designed to simulate the experience of using real ink. Users can select the thickness and color of the ink they use to write a document.

Drawboard PDF has several editing options. These include multiple image resizing tools, such as rotating, embedding, cropping, and eliminating pages.

The features of Drawboard Projects are more applicable to collaborative teamwork. The app supports PDF annotations, illustrations, revisions, and sharing.

  • PDF editing : Modify the information on a PDF using an image resizing tool or highlighter.
  • Collaboration: Work with other team members to create and edit PDF documents.
  • File sharing: Create a shareable link for your edited documents.
  • Cloud storage: Edited documents are stored on the platform and accessible from anywhere.
  • Easy to navigate
  • Several PDF editing capabilities
  • Relatively expensive
  • Ideal for architects and business teams
  • Not suitable for development teams

Drawboard has three pricing tiers and is not a gratis product.

The basic plan costs $20 a month and offers limited access to drawings and templates for projects.

The standard plan costs $35 a month and includes unlimited project templates.

Drawboard’s enterprise plan for businesses is priced according to the customer’s feature requirements.

Red Pen is a visual feedback tool that allows teams to communicate their ideas quickly. The app supports unlimited collaborators on a document, so you can invite as many individuals as possible to review your project.

Red Pen preserves the history of your edited images and creates new versions as you edit. The app is compatible with both Windows and Mac.

  • Visual feedback tools: Edit and annotate images to provide feedback.
  • Image edit history tracking: Follow the changes made by other collaborators.
  • Optional free trial period
  • Real-time commentary utility
  • Occasional bugs
  • Not available on mobile devices

Red Pen has four pricing tiers. The first plan allows five concurrent projects and costs $20 per month. The second plan is limited to 10 projects and costs $30 a month.

The third plan has a monthly subscription fee of $60 and is limited to 25 projects. The fourth plan supports 40 projects at $90 per month.

Go Visually

GoVisually is a must-have application for marketing and creative teams. It supports proofing, design, and video collaboration.

Users can upload files in PNG, JPG, PSD, PDF, Video MP4, and GIF formats, annotate them with various tools, and share the review link.

GoVisually enables users to categorize — and assign — design and video revisions according to their requirements. To facilitate content revisions, team members can examine comment histories as well as update the file status to indicate whether it’s pending a review or adjustments, or if it’s been approved or finalized.

  • File sharing and collaboration: Create files, then invite reviewers or team members to provide feedback.
  • Document upload: Add files to your workspace to instantly edit and collaborate.
  • Screen captures and annotations: Capture and annotate web pages or PDFs.
  • Minimal learning curve
  • Automated email notification
  • Limited integrations

Pricing starts at $20 per month.

Dubbed the world’s most intuitive video-led collaboration solution, Wipster enables teams to collect feedback primarily through videos. It also supports feedback sharing through images, PDFs, and audio.

Creative teams love Wipster for its video-proofing features. The app allows users to invite an infinite number of collaborators and reviewers.

  • Cloud storage and synchronization: Create and save files to the cloud for easy access.
  • Video feedback tool: Provide feedback with video and collaborate with team members.
  • Annotation and image edit: Modify images to suit your needs.
  • Collaboration: Invite team members and external stakeholders to work together on a document.
  • Supports integrations with external apps like Adobe
  • Organized feedback
  • Quick editing abilities
  • Free plan available
  • Doesn’t support issue tracking for dev teams

In addition to the free plan, Wipster has three price tiers: team, multi-team, and enterprise.

Free plans offer 5GB of cloud storage space and are recommended for occasional users. The team plan, which offers 250GB and many additional features, serves small and growing teams at $19.95 per user per month.

The multi-team plan costs $39.95 per user per month, with over 500GB of storage. It supports multiple teams and org-level administration.

Doccano is a text annotation tool for creating in-text notes and feedback. Developers typically use this open-source application to create machine-learning models.

Text analysis, sequence labeling, text summarization, and entity identification (including names, places, and more) are some of Doccano’s primary functions.

Doccano also supports language translation, allowing users to annotate text files in several languages.

  • In-text annotations: Highlight and annotate text within the document.
  • Text translation: Translate specific words or phrases to a different language.
  • Multi-language support
  • Only manual annotation

Since Doccano is an open-source data annotation tool, anyone can use it for free.

Online Annotation FAQs

How does annotation work.

Annotation is the relatively easy process of leaving visual- and text-based notes and feedback on a platform, document, or website. However, the steps you’ll need to follow when annotating will depend on the tool you’re using.

Certain annotation tools are meant for text-based documents only which are great for PDF annotation, others are designed for image annotation or video annotation, and others support the use of screenshots and screen captures to add more detail and context to your comments.

What is the Annotation Process followed for Image Annotation?

When referring to machine learning, image annotation and image annotation tools allow for images to be marked up with labels to train Artificial Intelligence.

It can also refer to the process of leaving comments (or image annotation) on a static image, a design or on a video during a design element review as an easy way to leave feedback or point out changes required.

Using image annotation tools makes the process easier. How? Here's a simple annotation process for image review:

  • Upload your images for review to a central shared location. (Make sure they're acceptable file formats.
  • Invite reviewers to leave feedback using an image annotation tool. Reviewers can simply add contextual comments pinned to the image with a simple point-and-click.

What Is the Best Software for Annotation?

This will depend on the particular needs of your project. This will depend on the particular needs of your project. Popular image annotation tools or text only annotation software might not always be exactly what you're looking for.

Consider BugHerd if you're looking for multiple annotation types for web annotation projects that encompass the entire website design and development process. This includes Design, image selection, QA testing, bug reporting & issue tracking, through to user acceptance testing.

BugHerd allows users to provide visual and video feedback, supports detailed issue reporting and tracking, and integrates with other business tools like Slack and Asana. These features make BugHerd ideal for web developers and project managers who need a tool that’s optimized for collecting stakeholder and customer feedback.

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list of annotation tools

What is BugHerd?

BugHerd is the best tool to collect, organise and act on website feedback. It's bug tracking software that's like sticky-notes on a webpage.

list of annotation tools

List of Open-Source Annotation Tools for Machine Learning Research

There are quite many annotation tools out there, however it seems like each and every tool comes with their unique disadvantages. Anyhow, here is a list of tools I came across. And yes, I only post tools which are open-source. I highly recommend to write your own project-specific annotation tool that automate away as much as possible. This may take a week or two (or three ;)) but is much more efficient.

last update of this list: 2022-08-20

Computer Vision

  • General purpose

Point Clouds

Source code.

  • simple annotation tool to label waveforms. contains some processing capabilities as well
  • transcription and labeling for audio data
  • webinterface for audio annotation
  • web-based annotation tools for waveforms
  • web-based annotation tool for audio files
  • annotation for sets of audio
  • Praat: doing phonetics by computer. Larger framework for phonetics that has some annotation capabilities
  • Annotation tool for 6D pose estimation
  • Yolo type annotations
  • semi-automatic bounding box labeling with tensorflow/keras backend
  • COCO keypoint annotations for images seems to be defective
  • small size JS annotation library for web-based image annotation
  • auto-annotate based on Mask R-CNN (COCO seems to work out of the box)
  • bounding boxes for videos
  • fast bounding boxes for Yolo, Pascal VOC and COCO
  • aims at annotation of bounding boxes and segmentation, part of OpenCV
  • DeepLabCut is full software suite for animal pose estimation (humans included). Besides a full inference pipeline it does contain an annotation tool.
  • bounding boxes for images and videos
  • bounding box annotation tool
  • annotation tool for facial keypoint annotations
  • points and bounding boxes
  • annotation tool that allows plugins
  • bounding box annotations
  • collaborative image labeling originating from RoboCup competitions. Mainly bounding boxes
  • JavaScript based image segmentation annotator
  • annotation tool for 3D images
  • polygon annotation similar to LabelMe but as a desktop software
  • bounding boxes for PASCAL VOC
  • PASCAL VOC annotation tool
  • underlying annotation tool of the LabelMe platform
  • offline fork of LabelMe
  • bounding boxes and polygons
  • bounding boxes
  • “LOST - Label Objects and Save Time” that supports semi-automatic annotation
  • webbased image annotation tool with some auto-annotation features
  • for annotation of medical (image) datasets.
  • PASCAL VOC bounding box annotations
  • annotations for object detection and object tracking
  • Segmentation annotation tool written in Qt
  • annotation tool for pixel-level segmentation annotation
  • supports annotation of bounding boxes, polygones and semantic segmentation.
  • pose annotations
  • supports annotation of bitmaps and point clouds
  • semi-automatic object segmentation on videos
  • tool for image and video related annotation tasks
  • bounding box tool for Mechanical Turk
  • multi-purpose video annotation tool
  • video object annotations
  • bounding boxes for images and videos with a focus on creating datasets for object motion detection
  • browser-based YOLO annotation tool
  • image annotation for Yolo v2 & v3

General Purpose

  • general purpose annotation tool that supports labeling for audio, computer vision and NLP applications with an integrated ML backend for active learning
  • 3D point cloud and 2D (pseudo 3D) image annotation (annotations similar to self-driving car datasets)
  • semi-automatic point cloud annotation
  • annotating 3D point clouds using VR (Oculus Rift)
  • 3D bounding box annotator for point clouds
  • ROS-based multi-label point cloud annotation tool
  • source code annotation tool
  • annotation library for annotating websites. could be utilized for machine learning text annotation as well
  • text annotation tool
  • standard text annotation tool
  • general purpose NLP annotation tool
  • NLP annotation for PDFs
  • web-based text annotator
  • simple QT gui for image annotation (classes only?)
  • text annotation for SpaCy
  • collaborative text annotation
  • time series annotation
  • annotation of time series anomalies
  • DNA sequence annotations
  • annotation tool for audio, computer vision, nlp and data in general

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A curated list of awesome annotation tools. Please feel free to pull requests to add links.

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7+ Best Annotation Tools of 2024 (FREE and PAID)

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Annotation tools can be used for many purposes, such as research, study, collaboration, and sharing information. The use cases for annotation tools are endless. In this guide, I ranked and reviewed the 8 best annotation tools , along with my top 4 choices, so that you can pick the best one

The 8 best annotation tools are  Adobe Acrobat Pro DC , Markup Hero,  Annotate , Filestage , zipBoard ,  ClickUp ,  PDF Annotator , and Hive.

Adobe Acrobat Pro DC

Free 7-day trial

Best for individuals and businesses

  • Markup Hero

Free version 

Best for freelancers and businesses

Web, Windows, and Mac

Best for enterprises

Contact me , if you want your product to be reviewed

Annotation tools are software that helps annotate and track the changes in documents. They can be used for a variety of purposes, including reviewing changes, tracking changes, and collaborating on documents.

They can be used in a variety of fields such as education, business, law, medicine, and more.

Annotation tools can be divided into two types:

– Textual annotation tools : these tools allow users to annotate texts by adding comments, highlighting passages, or underlining words.

– Visual annotation tools : these tools allow users to annotate images or videos by drawing on them and adding text, arrows, and shapes.

There is a wide range of annotation software available on the market, each with its own set of advantages and disadvantages, so you must carefully plan and access each one to choose the one that best meets your demands.

Let’s get started reviewing the top annotation tools.

Comparison of Best Annotation Tools

What are the best annotation tools.

The eight best annotation tools available are:

  • Adobe Acrobat Pro DC
  • PDF Annotator

1- Adobe Acrobat Pro DC

Adobe Acrobat Pro DC is a powerful tool that offers annotation tools, PDF conversion, and editing features. It is a must-have for any business or organization that needs to produce complex documents or deliver reports.

PDFs are often used in business to distribute important documents, but they can be difficult to work with when it comes to editing them. With Adobe Acrobat Pro DC, you can easily annotate PDFs and make changes without having to worry about compatibility with other programs.

The annotations can be text comments, highlighting, or drawing shapes on the document with your mouse cursor. You can also add stamps like “confidential” or “draft” on top of the document.

This annotation tool doesn’t have a FREE version however it provides a 7‑day free trial.

list of annotation tools

Best for individuals and businesses of all sizes.

Key Features:

  • Add notes, highlights, and other annotations
  • Edit text and images in your PDF.
  • Compress PDF
  • Recognize Text (OCR)

Adobe sign divides its pricing into two categories

1- Individual:

Acrobat Standard DC

  • $12.99 annual commitment billed monthly
  • $22.99 no annual commitment required. Windows only.

Acrobat Pro DC

  • US$14.99/mo requires an annual commitment. Windows and Mac.
  • US$24.99/mo no annual commitment required. Windows and Mac.

Acrobat Standard DC for teams

  • US$14.99/mo/license  requires an annual commitment. Windows only.

Acrobat Pro DC for teams

  • US$16.99/mo/license  requires an annual commitment. For Windows and macOS.

Acrobat Sign Solutions

  • Call for pricing

Adobe Acrobat Pro DC Pricing

2- Markup Hero

Markup Hero is a fast and easy annotation tool that allows you to markup images and PDFs with arrows, shapes, and text. With Markup Hero, you can quickly annotate images and PDFs without having to download or install any software.

It is a web annotation tool that is designed to help content creators and marketers annotate and share their work on the internet. With this tool, you can easily annotate and collaborate with your team members in order to produce content fast.

They also provide applications for Mac, Windows, Linux, and Chrome and support a FREE plan which makes it super convenient.

Best for individuals and small businesses.

  • Scrolling screenshots
  • Text, arrows, highlighters, icons annotations
  • Create your own team or invite other people to collaborate with you in real-time
  • Get notified when someone has commented on your annotation
  • Integration with Google Drive and Slack
  • API available

Markup Hero comes with 2 different plans

  • Hero : Free for individuals and up to 10 markups per month
  • Superhero : $4 unlimited usage, best for teams.

Markup Hero Pricing Plans

3- Annotate

Annotate is a web annotation tool that allows you to highlight, underline, and comment on any document. With its very easy-to-use interface and powerful features, Annotate that become the go-to annotation tool in the market.

Annotate is the best way to annotate PDFs. It’s simple, efficient, and easy to use. With Annotate, you can quickly and easily add annotations to any PDF document.

In addition to the annotation tool, it offers other features like document management, document workflow , and so on to make working with documents easier and to assist change your workplace into a digital one.

For all offices and data centers, Annotate adheres to the appropriate international risk management requirements.

Best for individuals and businesses.

  • Document collaboration
  • Document management
  • Native chat
  • Document annotation
  • Integration with other systems

Annotate comes with a FREE plan for up to three users and 30MB of safe storage space. For other plans, you need to contact them.

Annotate annotation tool

4- Filestage

Filestage is one of the best annotation tools that lets you create and collaborate on videos, images, PDFs, and web pages.

The Filestage annotation tool is an easy-to-use, web-based tool that helps you annotate PDF documents. It allows you to add text, highlight important sections, and add your own comments. The tool is perfect for collaboration, as it allows you to share your annotations with others.

The reviewing features allow other colleagues to directly evaluate the material and discuss in real-time, eliminating the need to manually send them an email for evaluation. This will undoubtedly assist to accelerate the cooperation process and boost productivity.

Best for individuals and small businesses and it doesn’t provide a free version. However, you can have a free trial period.

  • Unlimited reviewers
  • One-click approvals
  • Annotations
  • Project dashboard
  • Compare versions

It comes with four different plans, none of which are free.

  • Essential : $9 / month / seat (For freelancers)
  • Advanced : Starting at  $95  per month including the first 5 seats (For small teams)
  • Professional : Starting at  $195  per month including your first 5 seats (For large teams)
  • Enterprise : For companies and no pricing information

Filestage annotation tool

5- zipBoard

list of annotation tools

zipBoard is a web-based annotation and visual feedback tool for almost all types of content. It lets you annotate on images, videos, webpages (static and live), SCORM, PDFs, etc. and simplifies your visual bug tracking & QA process for reviewers. With zipBoard, you can review all your online proofs on a centralized platform and get rid of the endless email chains and screen-sharing meetings.

zipBoard’s vast array of annotation tools lets you markup directly on top of your screen and share them across the team and stakeholders. It has an easy-to-use, simple UI, especially useful while getting feedback from non-technical clients and stakeholders which is often the need.

All of these without the need to create an account for the guest collaborators, letting you assign and prioritize the tasks from its dashboard.

With the traceability of all versions and changes and a clear designation of roles, zipBoard helps ensure confusion-free collaboration for all, whether you’re an agency or an individual.

  • Unlimited collaborators and projects.
  • The biggest range of annotation tools.
  • Provide your stamp of approval and sign off creatives.
  • Integrate the product into your ERP. API creation on demand.
  • Best in class sharing features.

zipBoard comes with 3 different plans and they offer a 15-day free trial in each of them. Team, Agency, Enterprise: Custom ( Contact zipBoard )

Clickup is a web-based platform that enables its users to create, manage, and collaborate on projects.

Clickup is a web-based project management and collaboration tool that provides users with an easy way to organize their work. The basic version of Clickup is free for teams of up to five people.

The app has been designed for anyone who needs help organizing their work or collaborating with colleagues on projects. It’s ideal for small businesses, entrepreneurs, sales teams, freelancers, and more.

ClickUp has a long list of satisfied clients, including Samsung, IBM, and booking.com.

Ideal for individuals and companies of all sizes with a mobile/tablet app.

  • Powerful collaboration features
  • Annotate in real-time with others
  • Real-time reporting
  • Time management

ClickUp comes with 3 different plans.

  • FREE : Best for personal use
  • Unlimited : $5 / user / month. Best for small teams
  • Enterprise : You need to contact them. Best for many large teams

ClickUp annotation tool

7- PDF Annotator

PDF Annotator is a PDF reader with many tools for annotating, filling out and editing PDFs.

PDF Annotator is a simple but powerful annotation tool, which helps users to fill out, annotate and edit PDFs. It also provides many helpful features to make the editing process easier. You can add text annotations in the form of text boxes or sticky notes, highlight text passages or draw shapes on the document.

PDF Annotator is easy to use and has many features that make it stand out from other annotation tools. It also offers a free trial so you can try it before you buy!

Best for individuals and businesses and only available as a Windows application.

  • Easy to use
  • Good annotation capabilities
  • Adding custom images

This annotation tool doesn’t have a FREE version however, they have a 30-day trial to test it out.

  • Business: $69.95 excluding VAT per year
  • Education: $39.95 excluding VAT per year

PDF annotator

Hive is a project management software package with a variety of team-friendly features. Annotation is one of these functionalities, which allows users to collaboratively annotate and edit documents in real-time.

Hive is a project management and annotation tool that is designed to help you and your team stay organized and collaborate better. Hive is a simple, yet powerful tool that will help you manage your projects from start to finish.

With Hive, you can upload files, create tasks, plan your timeline, annotate documents, and more. It’s the perfect tool for anyone who needs to get their work done with minimal effort.

  • Project management
  • Annotation capabilities
  • In-App messenger
  • Proofing & approvals
  • In-App email
  • Automate repeated processes
  • Good integrations capabilities

Hive comes with 3 different plans and the most important is that it has a FREE version.

  • Hive Solo : Free forever
  • Hive Teams : $12 / user / month
  • Hive Enterprise : You need to contact them

Hive Annotation tool

What is annotation tool?

The annotation tool is a software that helps to organize and manage information. It can be used for any type of information, such as text, images, audio and video. It lets you add notes or comments on the content in order to make it easier for you to find the needed data when you need it.

7 Best Document Collaboration Tools of 2024 (FREE and PAID)

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May 3, 2022

Ultimate list of free and paid best image annotation tools

What are annotation tools and where are they used? 

Judging by the current rate of data creation, data annotation is no easy task, and the ability of the algorithms we use today to perform effectively depends on data annotation. Computers can't interprete situations or provide context the way humans can so data annotation was birthed to make those connections. It is the human-in-the-loop approach of labeling content like text and audiovisuals (images and video) so they can be correctly recognized by ML models and used to make accurate predictions. 

What is image annotation 

Image annotation in machine learning and deep learning can be defined as the process of annotating an image with labels, usually involving a human-in-the-loop approach and the occasional computer assisted help. It involves classifying an image using annotation tools, to show the data features you want your model to recognize on its own and it is mainly done so the system will be able to recognize objects with greater precision. This is usually done with datasets that are used to train ML algorithms. 

Difference between image annotation and labelling 

Image labeling and image annotation are phrases used interchangeably when trying to describe the art of classifying or identifying images for training machine learning algorithms. Both processes need to be executed with great precision and clarity with their major difference being that annotation helps the system recognize relevant data through computer vision whereas labeling is used for training advanced algorithms to recognize patterns in future so it'll be able to make decisions on its own.

3 free image annotation tools

1. Isahit lab: This is an image annotation tools that is extremely easy to use which allows you to annotate an unlimited number of images for free. It also allows you to invite teams and users to assist you on your annotation projects. Isahit spent years developing this intuitive annotation tool and were able to draw from their experience in handling labeling projects through their outsourcing platform.

2. CVAT: CVAT is an acronym which stands for Computer Vision Annotation Tool and it is a free open source annotation tool which is developed by Intel and it also supports video annotation. It also has a user-friendly interface which makes annotating easier. To start using CVAT,  you'll need to create an account first on cvat.org after which you'll be given access to the tool and you can start annotating your data.

3. Labelme: Labelme is an open source annotation tool that you could use to process image datasets. A key feature to be noted on the tool is the “File List” option on the bottom right. This could come in handy when you have a lot of images to annotate, because it reduces the chances of you missing any images. The only disadvantage of labelme is that your file can only be saved in JSON format. 

3 paid image annotation tools

1. V7: V7 is an automated annotation tool that combines dataset management, image and video annotation, and auto machine learning model training to complete annotation tasks. The platform enables teams to store, manage, annotate, and automate their data annotation workflows in videos, images, medical data and other formats. Price options start from $150.

2. Labelbox: Labelbox offers AI-powered labeling tools, labeling automation, human workforce, data management, among other services along with a powerful API for integration. The platform offers a superpixel coloring option for semantic segmentation and a friendly user interface.

3. Data loop: This is an all-in-one cloud-based annotation platform with embedded tools and automation capable of producing high-quality datasets. The platform makes provision for the entire AI lifecycle including annotation, model evaluation, and model improvement by using a human in the loop approach. It also offers tools for basic recognition tasks like detection, classification, key points, and segmentation while also supporting both image and video data. It also has advanced team workflows with streamlined data indexing and video support.

Key criteria for reviewing image annotation tools

When doing image annotation, it is very important that you choose a very good tool as it will directly affect the quality of your processed work. There are certain criteria you should take into consideration such as functionality, efficiency, formatting, application and price to be able to find a tool that adequately fits your needs. There are many image annotation tools out there and you have to do your research carefully to make sure you choose a tool that works best for you and your project. 

Outsourcing image labeling - The solution for large-scale annotation projects

If you have a large volume of images that need to be labeled, outsourcing can be a smart solution. Companies like isahit offer image labeling services that not only provide you with the annotation tool, but also a trained and qualified workforce to annotate your data accurately and efficiently.

With isahit, you can trust that your annotation project is in good hands. Their diverse and qualified workforce is committed to providing you with high-quality labeled data, ensuring that your project is completed on time and to your satisfaction. By outsourcing your image labeling needs, you can save time and resources, and focus on other important aspects of your project.

Each unique project has a specific need when it comes to annotation tools. One might need either an image annotation tool, a text annotation tool, a video annotation tool or some combination of the above. With so many tools to choose from, finding the right fit can be a frustrating process. Not to worry though- we’ve rounded up a master list of the best open source annotation tools in 2022. Keep reading to find the best annotation tool for your unique needs!

Best Image and Video Annotators

The Computer Vision Annotation Tool is a powerful and efficient image and video annotator. It is open source and web based, and though its user interface is not very intuitive, amateurs and professionals will be able to take advantage of it after getting over the learning curvet.

Key Features:

  • Maintained by Intel
  • Annotation is semi-automatic
  • Third Party Integrations
  • Easy to deploy on local networks
  • Tutorials available online
  • Supports a variety of file types
  • No customer support
  • CVAT has to be maintained as it scales
  • No pdf file support
  • Learning curve; the UI may take several days to master

Source code- https://github.com/opencv/cvat

Labelimg has been around for over 5 years, and is one of the popular, dependable tools for graphic image labelling on the web. It has a simple interface which is also pretty intuitive, making it pretty easy to work with.

Key Features

  • Written in Python
  • Graphical Interface is in QT
  • Annotations are saved and exported as XML files
  • Needs to be installed locally
  • Can be used offline for added security
  • Simple, intuitive interface, even for beginners
  • Default version offers only one annotation type (bounding box)

Does not offer video annotation

Source code- https://github.com/tzutalin/labelImg

LabelMe is an open source dataset of digital images with annotations. Free to use, it was created by the MIT Computer Science and Artificial Intelligence Laboratory in 2008, and users are allowed to contribute to the library. It has a voluminous library, described by some as canonical.

  • Made by MIT Computer Science and Artificial Intelligence Laboratory
  • Six different types of annotations offered
  • Customizable UI
  • Can be used both online and offline
  • Files can only be exported and saved in JSON format
  • No program management capabilities
  • Low level of precision

OpenLabeling  

OpenLabeling is a sturdy tool for both image and video annotation in computer vision applications Created by João Cartucho, this tool was licensed in 2018.

  • Runs in Python
  • Features a pre-trained model 
  • Multiple annotation formats are available, for example PascalVOC and YOLODarknet
  • Deep Learning feature available
  • Both Python and OpenCV have to be downloaded to use this tool

Source code- https://github.com/Cartucho/OpenLabeling

Best Text Annotation Tools

Developed to annotate chunks of text, YEDDA is able to work in many languages including English and Chinese. Text, symbols and even emojis can be accurately annotated by this super tool.

Yedda also supports shortcut annotation which increases efficiency in annotating text by hand.

  • Runs in Python, requires Python preinstallation
  • Supports shortcut annotation
  • Collaborative capability
  • User Interfaces both for administrators and annotators
  • Users complain of a few bugs while using this tool
  • Not available offline

Source code https://github.com/jiesutd/YEDDA

ML-Annotate

Another popular open source text annotator, ML-Annotate is one of the first choices for many when it comes to text annotation. Developed by 

  • Runs on Python
  • Administrative users can be added
  • UI is completely customizable, with instructions for making modifications provided
  • Supports multi-class,multi-label and binary labelling
  • Can be used offline
  • Data library not included.

Source code https://github.com/falcony-io/ml-annotate

We hope this was helpful! If you’re still undecided, you can check out our table below for the summarised version of all the info above.

Image and video annotation comparative table

list of annotation tools

Your AI project's success or failure will be determined by the data annotation tools you employ to enrich your data for training and deploying machine learning models. 

What is data annotation?

The process of labeling data to show the results you want your machine learning model to forecast is called data annotation in machine learning. You're marking up a dataset with the qualities you want your machine learning system to learn to recognize by labeling, tagging, transcribing, or processing it. 

What is a data annotation tool?

A data annotation tool is a solution that can be used to annotate industrial-grade training data for machine learning. It can be cloud-based, on-premise, or containerized. The following are essential features of data annotation software: Text, image, video, audio, time-series, and sensor data are all examples of data types that can be annotated with them. They support 2-D, 3-D, video, audio, transcription, and text annotation.

Important features of a data annotation tool.

1. dataset management:.

Annotation begins and ends with a thorough understanding of how to manage the dataset you're going to annotate. You must confirm that the tool you are contemplating will really import and support the large volume of data and file formats you need to label as a crucial element of your process. Datasets can be searched, filtered, sorted, cloned, and merged using this method.

2. Data quality control:

The quality of your data will determine the performance of your machine learning and AI models. Quality control (QC) and verification processes can be made easier with data annotation tools. Ideally, the tool will include quality control as part of the annotation process.

3. Data security:

Whether you're annotating sensitive protected personal information or your own valuable intellectual property, you'll want to keep your data secure. Tools should restrict data downloads and limit an annotator's viewing rights to data not assigned to them.

Tips for selecting a data annotation tool

1. Figure out what you're going to use it for: First and foremost, the type of data you wish to annotate as well as your work procedures will influence your tool selection. Text, image, and video can all be labeled with tools. Video labeling is possible with some image labeling software. As a result, select a tool based on your objectives.

2. What are the requirements for quality control?

Your data annotation tool should also take into account how you wish to measure and control quality. Quality control (QC) elements are included into many commercially accessible tools, and they can review, provide feedback, and correct activities.

3. Workforce training: Whether your data is annotated by employees or contractors, crowdsourcing, or an outsourcing provider, your workforce will need access to and training to use your data annotation tool, with specific instructions particular to your use case.

3 free data annotation tools

1. computer vision annotation tool (cvat):.

CVAT is a web-based, free, open-source annotation tool that can be used to annotate image and video data for computer vision algorithms.

It has a dashboard with a list of annotation projects and tasks, as well as interpolation of shapes between keyframes, shortcuts for the most important activities, and a dashboard with a list of annotation projects and tasks. The primary tasks of supervised machine learning are supported by CVAT. Object identification, image classification, and image segmentation are all things that can be done with images.

2. Kili Technology :

Kili Technology is a labeling platform for training data. Kili Technology offers one tool to label, find and fix issues, simplify DataOps, and dramatically accelerate the build of reliable AI.

Kili Technology offers 3 plans for these customers, a free offer limited to 5 users and 1000 annotations per month and two paid plans from 20 000 to 500 000 annotations included as well as the addition of an external work force. Kili Technology and isahit are main partners, Kili is isahit's main technological partner, and conversely, isahit provides Kili with a diversified, competent and committed workforce.

3. Labelstudio :

Labelsudio is a flexible data labeling tool for all data types. Prepare training data for computer vision, natural language processing, speech, voice, and video models.

3 paid data annotation tools

1. labelbox.

Labelbox's training data platform is designed to assist you in improving your training data iteration loop. It's built around three main pillars: the ability to annotate data, diagnose model performance, and prioritize tasks based on your findings. By using the latest in labeling automation, you can reduce annotation costs by 50-80%, iterate 3 times quicker on your AI data to construct more performant models, and work more efficiently with data scientists, labelers, and domain experts with Labelbox.

Scale is a data platform to annotate enormous amounts of 3D sensor, picture, and video data. This data annotation tool supports several data formats and can be used for a range of computer vision applications, such as object detection, classification, and text recognition. Its advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companiesto focus on building differentiated models vs. labeling data.

V7 is an automated annotation platform that combines dataset management, image and video annotation, and autoML model training to perform labeling tasks automatically. V7 allows teams to store, manage, annotate, and automate data annotation operations in pictures, video, medical data, microscopy images, PDF and document processing, and other formats.

Want to explore more tools? We share with you this curated list of data labeling tools !

Scale your digital annotation projects with a highly trained workforce

In-house annotation can limit you in terms of volume and create some bias in annotation.

Today, data labeling companies can make all the difference in the training of your algorithms: by training and coaching a diverse, competent and committed workforce supported and challenged by a project team that follows the quality of the annotations and monitors your projects daily.

Moreover, outsourcing your annotations can also be an opportunity for the company to generate a positive social impact among the annotators! Isahit is the first and only player in the industry to offer an agile, socially responsible data labeling service powered by human intelligence.

They build, train and deploy a customized and diverse workforce on their clients' digital projects: data labeling, algorithm training, etc. while generating a real positive impact with their workforce: an additional income 5x higher than the average in their country, free trainings and a caring community to lean on. BCorp certified since 2021, isahit is revolutionizing the world of data tagging and outsourcing by making it ethical. ‍

Conclusion.

Your data annotation tool's sophistication and features have an impact on how you and your data team plan workflow, quality control, and many other areas of your data work. A tool that does not address your workforce and processes will cost you time and efficiency in the form of workarounds for features you wish were inherent to the product. Before choosing your tool, be sure to check the tool's functionality and define your needs! And if you want to scale up your annotation projects, use an external, competent and committed workforce (with ethics)!

Access a trained Workforce, managed ethically.

Ethically scale your digital annotation projects with our highly trained workforce. Access our On-Demand Workforce to get the best quality in your Dataset Labeling.

list of annotation tools

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Best DICOM Labeling Tools [2024 Review]

Review of 6 best medical ai annotation tools for dicom, encord dicom, dicom annotation tools: key takeaways .

blog image

The FDA has approved over 300 AI algorithms over the last 4 years – the vast majority of which relate to medical imaging. 

With the increase in medical AI and computer vision applications, healthcare teams are turning to AI models for more accurate and faster diagnosis at scale. 

A correct or incorrect diagnosis impacts treatment, care plans, and outcomes. And ultimately, computer vision and machine learning applications across medical AI have the potential to materially impact the chances of a positive outcome. 

And as we know, it all starts with data. Getting a radiology AI product to market – not to mention through FDA or CE clearance – starts with data quality and speed, which in turn relies heavily on accurate annotation and labels, whether the images come from CT, X-ray, PET, ultrasound, or MRI scans. 

To help you navigate all the DICOM labeling tools and frameworks on the market, we have compiled a list of the most popular annotation tools for annotating DICOM and NIfTI files .

Whether you are:

  • A data science team at a fast-growing radiology AI startup trying to bring your first products to market or obtain FDA approval
  • A data operations team at a large healthcare organization evaluating medical imaging tools to help your team analyze CT scans and MRI scans 
  • ...or a computer vision team at a healthcare provider or vendor delivering high-value machine learning-based solutions for hospitals, doctors, and other medical professionals. 

This guide will help you compare the top tools to annotate DICOM and NIfTI files and help you find the right one for you.

We will compare them across a few key features – collaboration, quality control (QC) and quality assurance (QA), and ease of use for annotators and medical data operations managers. If you’re evaluating NIfTI labeling tools, you can find more about the key features you need to look out for here .

So let’s get into it! In this post, we’ll cover six of the most popular AI-based medical image annotation tools:

Encord is the leading DICOM annotation platform trusted by leading medical AI teams at healthcare institutions. 

Encord’s AI-based annotation tool was purpose-built in close collaboration with healthcare teams for machine learning and computer vision projects in the medical profession. Encord and Encord Active are designed to handle vast medical image and video-based datasets (e.g. surgical video), alongside DICOM, NIfTI and +25 other data formats.

Benefits & Key features:

  • Native DICOM rendering: Render 20,000+ pixel intensities natively in the browser with a PACS-style interface.
  • 3D views: Multiplanar reconstruction (axial, coronal, and sagittal views) and maximum intensity projection (MIP).
  • Windowing support: Preset window settings for numerous modalities and the most common objects that need detecting, identifying, and annotating (e.g., lung, bone, heart, brain, etc.).
  • Hanging protocols support: For Mammography, CT and MRI.
  • Expert review workflows: Collaborative workflows designed for medical teams and scalable data operations.
  • Foundation models support: Generate mask predictions with our AI-based auto-segment tool.
  • Configurable labeling protocols: Create complex medical labels and protocols to train your annotation team with our medical-grade annotation tool. 
  • Support for multiple annotation types: Bounding boxes, polygons, segmentation, polylines, keypoints, object primitives, and classification. 

Best for: Teams rolling out new healthcare AI models, computer vision DataOps teams, annotation providers, ML engineers, and data scientists in medical organizations.

Pricing: Free trial model and simple per-user pricing after that.

💡 More insights on labeling DICOM with Encord:

Here are some examples of healthcare and medical imaging projects that Encord has been used for:

  • Floy , a radiology AI company that helps radiologists detect critical incidental findings in medical images, reduces CT & MRI annotation time with AI-assisted labeling.
  • RapidAI reduced MRI and CT annotation time by 70% using Encord for AI-assisted labeling. 
  • Stanford Medicine cut experiment duration time from 21 to 4 days while processing 3x the number of images in 1 platform rather than 3

Further reading:

  • Best Practice for Annotating DICOM and NIfTI Files
  • The 7 Features to Look Out For When Choosing a DICOM Annotation Tool

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3D Slicer is an open-source software application designed for medical image processing and visualization. It provides a platform for 3D image segmentation and registration. The US The National Institutes of Health (NIH) and other healthcare partners have played an important role in funding 3D Slicer, alongside Harvard Medical School, and dozens of other public and private funding sources. 

There have been numerous contributors to 3D Slicer, with an active community improving the source code, architecture, building modules, securing funding, and citing 3D slicer in medical computer vision and machine learning model training experiments and development.  

Labeling DICOM images with 3D Slicer

  • Easy (& free) to get started labeling DICOM files.
  • Great for manual data annotation — also supports semi-assisted labeling.
  • Robust ground-level annotation capabilities (including classification and object detection) for a broad set of computer vision use cases.

Best for: Students, researchers, and academics testing the waters with DICOM annotation (perhaps with a few files or a small open-source medical imaging dataset).

Pricing: Free!

💡 More insights on image labeling with 3D Slicer:

If your team is looking for a free annotation tool, you should know… 3D Slicer is one of the most popular open-source tools in the space, with over 1.2 million downloads since it was launched in 2011.

Other popular free image annotation alternatives to 3D Slicer are CVAT, ITK-Snap, MITK Workbench, HOROS, OsiriX, MONAI and OHIF Viewer.

If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — as key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are not available in open-source tools.

  • Buy vs build for computer vision annotation - what's better?
  • Overview of open-source annotation tools for computer vision

Labelbox is a US-based data annotation platform founded in 2018, after the founders experienced the difficulties associated with building in-house ML operations tools. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services. 

Teams can annotate a wide range of data types (PDF, audio, images, videos, and more) using the Labelbox data engine that can be configured for numerous ML, AI, and computer vision use cases. 

Using Labelbox to annotate DICOM images

  • Support for two annotation types – polyline and segmentation – and common imaging modalities – CT, MRI, and ultrasound. 
  • SaaS or on-premise workflows with privacy and security built-into the platform. 
  • Catalog view to help medical annotation teams label and sift and find patterns within vast multi-format datasets. 

Best for: Teams wanting to annotate other file formats alongside DICOM, like documents, video, text, audio, and PDFs.

Pricing: 10,000 free LBUs to begin with, and custom pricing beyond that.

💡 More insights on labeling DICOM with Labelbox:

If your team is looking for on-demand labeling services, you should know… Labelbox can connect your in-house team with outsourcing partners for large ML annotation projects. 

If data security is a requirement for your annotation project… Labelbox comes with enterprise-grade security as standard for healthcare and AI teams. 

  • Top 10 Free Healthcare Datasets for Computer Vision
  • 3 ECG Annotation Tools for Machine Learning

Kili is a data annotation platform founded in 2018 by a French team who had previously built the AI company, MyElefant, and an AI lab from scratch for BNP Paribas. The platform allows users to create and manage annotation projects, monitor progress, and collaborate with team members in real time. Kili has been used by businesses across various industries, including healthcare, finance, and retail, to accelerate their AI development.

Labeling DICOM files with Kili

  • Support for multiple annotation types, including text, image, video, and audio. 
  • A platform designed to label, find, and fix data annotation issues and simplify DataOps for AI teams of every size.
  • For small-scale projects, DataOps can implement Kili with 5 lines of code to turn a machine learning workflow into a data-centric AI workflow. 

Best for: ML and DataOps teams across a range of sectors, either with in-house or outsourced teams. 

Pricing: Free tier for individuals, alongside corporate and enterprise plans for businesses. 

💡 More insights on labeling DICOM with Kili:

If your team is looking for an easy-to-integrate ML tool, you should know… Kili was designed to embed into ML workflows easily – it doesn’t have as many features as some computer vision SaaS products, but it integrates rapidly in a wide range of data tech stacks.

  • How to Annotate DICOM and NIfTI Files  
  • Medical Image Segmentation: A Complete Guide  

ITK-Snap is a free, open-source, multi-platform software application used for image segmentation. ITK-Snap provides semi-automatic segmentation using active contour methods as well as manual delineation and image navigation. 

ITK-Snap was originally developed by a team of students at the University of North Carolina led by Guido Gerig (NYU Tanden School of Engineering) in 2004. Since then, it’s evolved considerably, now being overseen by Paul Yushkevich, Jilei Hao, Alison Pouch, Sadhana Ravikumar and other researchers at the Penn Image Computing and Science Laboratory (PICSL) at the University of Pennsylvania. The latest version, ITK-Snap 4.0, was released in 2020, funded by a grant from the Chan-Zuckerberg Initiative. 

Annotating DICOM files with ITK-Snap

  • Manual segmentation in three planes.
  • Support for additional 3D and 4D image formats alongside DICOM, like NIfTI.
  • A 3D cut-plane tool for faster processing of image segmentation results and multiple images, including an advanced distributed segmentation service (DSS).  

Best for: Medical image annotation, students, and research teams.

  • 9 Best Image Annotation Tools for Computer Vision [2024 Review]
  • The Top 6 Artificial Intelligence Healthcare Trends of 2024

MONAI is an open-source, PyTorch-based framework designed for deep learning in medical imaging. The project was started in 2019 by NVIDIA, the National Institutes of Health (NIH), and other contributors. The framework provides various tools, including a labeling tool, to assist in the creation of annotated datasets for training deep learning models. 

MONAI’s labeling tool allows users to annotate images with 2D or 3D bounding boxes, segmentation masks, and points. The annotations can be saved in a variety of formats and easily integrated into the MONAI pipeline for training and evaluation. MONAI has gained popularity due to its ease of use and its ability to accelerate research in medical imaging.

list of annotation tools

  • Easy (& free) to get started labeling biomedical and healthcare images with the MONAI Label Server.
  • Capabilities for training AI models for healthcare imaging across a range of modalities and medical specialisms with two transformer-based architectures.
  • Convenient integrations through the MONAI Deploy App SDK. 

Best for: Medical imaging, annotation, and research teams that need an open-source healthcare AI platform. 

💡 More insights on labeling DICOM with MONAI:

If your team is looking for an open-source alternative to commercial tools, you should know… MONAI is designed as an AI-based collaborative platform with a suite of features you can host and deploy in a wide range of medical environments.  

If data security is a requirement for your annotation project… MONAI is better equipped than most open-source medical imaging projects with layers of enterprise-grade security. 

  • 7 Ways to Improve Medical Imaging Dataset
  • Guide to Experiments for Medical Imaging in Machine Learning

There you have it! The 6 most popular annotation tools for annotating DICOM. 

For further reading, you might also want to check out a few honorable mentions, both paid and free annotation tools:

  • Hive: Cloud-based AI tools for organizations that need to apply labels across a wide range of data types
  • Dataloop: Software to train and improve ML and AI models with extensive annotation capabilities
  • Appen: One of the oldest labeling services platforms on the market, launched in 1996
  • VOTT: An open-source tool with tags and asset export features compatible with Tensorflow and the YOLO format. 

Ready to improve the accuracy, outputs, and speed to get your healthcare AI models production-ready with DICOM annotations? 

Sign-up for an Encord Free Trial : The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams. 

AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today .  

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In 1985, the PUMA 560 surgical robot made history by assisting the team at Memorial Medical Center during a stereotactic brain biopsy, marking one of the earliest recorded instances of robotic-assisted surgery and astonishing the world. Fast forward to today — surgical robotic systems are supporting surgeons across a growing array of medical interventions, assisting surgeries in ways few people imaged a few decades ago. Over the past eight years alone, the Robotically-Assisted Surgical (RAS) Devices market has expanded from $800 million in 2015 to well over $3 billion today. From prominent healthcare organizations to cutting-edge research institutes, from rapidly growing startups to non-profit initiatives, diverse teams are busy developing innovative surgical robotic systems. Their goal is to enhance surgical efficiency, improve precision and, ultimately, deliver better outcomes for patients. The recent leaps in computer vision have also further spurred this growth, as artificial intelligence is rapidly entering the operating room and enabling these systems to better perceive and interpret visual information in real time and aid surgeons on a wider range of tasks. This article explores the landscape of AI applications in surgical video analysis, some of the key innovators in the space and the role of high-quality training data in the development of AI-assisted surgical systems. AI-Assisted Surgical Robotics Companies like Intuitive Surgical, creator of the Da Vinci Surgical System, led the way in the 1990s: Da Vinci was the first robotics system approved by the FDA, initially for visualization and tissue retraction in 1997 and later for general surgery in 2000. With over 6,000 robots installed worldwide and over $6b in annual revenue, Intuitive has dominated the surgical robotics industry for the better part of the last 20 years, transforming the industry and enabling patient outcomes that were previously impossible. Yet 2019 marked the start of some of its patent expiries, and with that, a wave of new entrants and innovators. The use of AI-assisted techniques in robotics now extends from preoperative planning, to intraoperative guidance and postoperative care, and has advanced significantly thanks to the close collaboration of surgeons, programmers, and scientists. Let’s discuss some of the major real-world applications and teams working in this field — starting with preoperative planning. {{surgical_video_CTA}} Preoperative planning Preoperative (pre-op) planning includes a range of workstreams — from visualizing the steps of the operation, to forming a plan to tackle navigation or improve precision. Machine learning and computer vision are being leveraged in pre-op planning in many ways: from rapidly analyzing the tabular and visual data of patients (like medical records or scans), to ensuring precise trajectory planning, optimizing incision sites, and gaining more insights into potential complications. Surgical planning begins with processing and fusing various medical imaging modalities, such as CT scans, MRI scans, and ultrasound scans, to generate a comprehensive 3D model of the patient's anatomy. Computer vision algorithms and deep learning models are then employed to quickly analyze this visual data and surface recommendations and risks with pursuing different surgical steps. Algorithms also enable surgeons to identify and segment specific anatomical structures and regions of interest from the imaging data (like organs, blood vessels, abnormalities, and other critical structures within the 3D model). This segmentation is crucial for surgical planning as it provides a clear visualization of the target area. From here, surgeons can explore different surgical approaches and plan the optimal trajectory for instruments and incisions, assessing the risk factors by quantifying the distance or overlap between the planned surgical path and nearby structures. Pre-op data can also be combined with intraoperative data to achieve surgical outcomes not otherwise possible. One of the most innovative end-to-end platforms is Paradigm™ by Proprio Vision, who just a few days announced the successful completion of the world's first light field-enabled spine surgery. Using an array of advanced sensors and cameras, Paradigm captures high-definition multimodal images during surgery and integrates them with preoperative scans to provide surgeons with real-time mapping of the anatomy. In addition to augmenting navigation capabilities during a procedure, Paradigm also collects large amounts of pre-op and intra-op data to inform future surgical decision-making and improve surgical efficiency and accuracy. {{light_callout_start}} You can read more about Proprio's announcement on their website here. {{light_callout_end}} Another end-to-end robotic system is Senhance, by Asensus Surgical, which in 2021 was cleared by the FDA for general surgeries. Senhance allows surgeons to create simulations for preoperative planning, while also providing real-time data for intraoperative guidance and generating insightful analytics for postoperative performance assessments and care. Intraoperative guidance A recent report by Bain & Company found that over 50% of surgeons surveyed made use of robotic systems in some capacity during general surgeries. During procedures, where even the slightest hand trembling can risk causing significant harm, image-guided surgery is turning into a requirement. Here, computer vision is often employed for instrument tracking and object recognition, which in turn are leveraged to feed video data to AI models that can monitor the procedure and generate guidance and warnings in case of anomalies, such as excessive bleeding or tissue damage. AI-assisted systems allow surgical robots to locate and follow the movement of surgical instruments, ensuring they are precisely positioned and maneuvered. They can also be used to identify critical structures and masses in the video footage, providing augmented guidance to the surgeon in real time. Model-assisted annotations of polyps in the Encord training data platform General and Minimally Invasive Surgery (MIS) Robotic assisted devices are more and more frequent in Minimally Invasive Surgeries (MIS). The primary objective of MIS is to reduce the trauma to the patient's body; the incision surface area is smaller, and often serves as an entry point, or port, for specialized instruments and a camera, known as a laparoscope, to enter the tissues and feed back real-time video data, which allows surgeons to view internal stuctures on a monitor and be guided through the procedure. MIS employs long, thin instruments with articulating tips that can be maneuvered through the small incisions. Systems like Dexter (by Distalmotion) are currently being used for daily gynecology, urology and general surgery procedures in Europe. “Surgeons can choose to operate entire procedures robotically, or they can leverage the ability to easily switch between the robotic and laparoscopic modalities to perform specialized tasks such as stapling with their preferred and trusted instruments,” Distalmotion CEO Michael Friedrich said in a recent press release announcing their upcoming US expansion. Another promising platform is Maestro (built by Moon Surgical), which sits at the intersection of robotic-assisted surgery and conventional surgery: acting as a robotic surgical assistant, it augments the precision and control of laparoscopic surgery, increasing the dexterity of a surgeon's own hand. Just this month, Moon Surgical announced the successful completion of the first 10 laparoscopic surgeries with its Maestro system in France. The procedures — bariatric and abdominal surgery procedures — were performed by laparoscopic surgeons Dr. Benjamin Cadière and Dr. Georges Debs, who said that the platform provided them with stability and precision that are difficult to match with human assistance. Many different procedure types are benefitting from the innovation in surgical assisted devices. A few examples are: Orthopedic Surgery. Orthopedic surgery is primarily used for the treatment of musculoskeletal conditions and disorders, mostly relating to bones and joints. With deep learning and computer vision, surgeons can build a pre-op model to plan the creation of patient-specific implants and the precise alignment of bones and joints, and then leverage a robotic arm to facilitate the optimal placement during the surgery. Stryker, the creators of the MAKO surgical assistant, are one of the pioneers in this space: MAKO turns a CT scan of a patient's joint into a 3D model, measures soft tissue balance, and, during surgery, ensures the placement is optimized to the patient's anatomy. Ganymed Robotics is another innovator in the space of orthopedic robotics. The Paris-based startup's team of computer vision and deep learning imaging experts have built a tool that leverages multimodal sensors to improve hard tissue surgery, starting with total knee arthroplasty (TKA). Robotic Bronchoscopy. Bronchoscopy helps evaluate and diagnose lung conditions, obtain samples of tissue or fluid, and remove foreign bodies. During a robotic bronchoscopy, the doctor uses a controller to operate a robotic arm, which guides a catheter (a thin, flexible, and maneuverable tube equipped with a camera, light, and shape-sensing technology) through the patient’s airways. Noah Medical received FDA clearance earlier this year for its Galaxy System™: a computer vision powered lung navigation system that improves the visualization and access of robotic brochoscopies. Microsurgery. Microsurgery requires the use of high-powered microscopes and precision instruments to perform intricate procedures on tiny structures within the body, such as blood vessel, nerve and tissue repairs. These kinds of surgeries operate hard-to-see anatomical structures that are often invisible to the human eye, and surgeons performing them need to undergo extensive training to develop exceptional hand-eye coordination. A handful of computer vision powered systems are being built to help improve the outcomes of these delicate surgeries, like MUSA-3, the microsurgery robot by Microsure, which allows surgeons to use a joystick to control instrument positioning during lymphatic surgery. The system is optimized for tremor-filtered movements and high-precision, and uses high-definition on-screen displays to enable real-time image analysis during these exceptionally delicate procedures. The Microsure team raised a €38m Series B earlier this month, as they eye FDA clearance in the US and CE-mark in Europe. Postoperative analysis and training Successful patient outcomes are achieved before, during, and after what happens in the operating room. AI surgical systems are valuable in post-operative analysis, as surgeons can review the process to understand improvement areas, identify potential health risks for the patient, and share insights to align expectations. Video data can also help trained newly formed surgeons, and provide education and knowledge share for the academic surgery community. Annotated surgical videos contain information regarding critical procedures, and can help inform students about effective surgical practices or risks involved with specific techniques. AI systems can also assess surgical performance by monitoring live video feeds and comparing a surgeon’s techniques with those used in similar procedures previously. The system can record custom metrics such as an operation’s total duration, patient satisfaction and post-operative complications, establishing benchmarks and shared understanding. A leader in this space is Orsi Academy, a Belgian training and research community that helps train medical professionals in new AI-driven techniques, such as computer vision for analyzing surgical videos, surgical data science for performance evaluation, and 3D printing, to simulation to help surgeons better understand and view specific body parts and surgical sites. Just a few days ago, Orsi Academy announced that their augmented reality tool (developed by Orsi Innotech) had enabled surgeons at Erasmus Medical Center to perform the world's first robot-assisted lobectomy using augmented reality, marking a huge achievement for the AI-assisted world of surgery. During this surgery, virtual overlay of the tumor, blood vessels and airways were projected over the camera image of the patient’s lung and was rendered with real-time AI-assisted robotic instrument detection. This allows surgeons to find their way inside the patient’s body more safely & effectively.  {{light_callout_start}} Orsi Academy will be hosting their annual Orsi Event in Belgium, on December 14th and 15th. Details will be available on their website shortly. {{light_callout_end}}

sampleImage_medical-image-segmentation

Medical image segmentation is used to extract regions of interest (ROIs) from medical images and videos. When training computer vision models for healthcare use cases, you can use image segmentation as a time and cost-effective approach to labeling and annotation to improve accuracy and outputs.  Segmentation in medical imaging is a powerful way of identifying objects, segmenting pixels, grouping them, and using this approach to labeling to train computer vision models.  In this guide, we’ll explore medical image segmentation, its role in healthcare computer vision projects, applications, and how to implement medical image segmentation.  {{Training_data_CTA::Accelerate your medical labeling workflow with advanced functionalities in Encord}} What is Medical Image Segmentation? Computer vision models rely on large training datasets used to train the algorithmic models (CV, AI, ML, etc.) to achieve high-precision medical diagnostics. An integral part of this process is annotating and labeling the images or videos in a dataset. One method for this is image segmentation, which this article will explore in more detail.  Medical image segmentation involves the extraction of regions of interest (ROIs) from medical images, such as DICOM and NIfTI images, CT (Computed Tomography) scans, X-Rays, and Magnetic Resonance Imaging (MRI) files.  There are numerous ways to approach segmentation, from traditional methods that have been around for decades to new deep-learning techniques.  Naturally, everything in the medical profession needs to be implemented with precision, care, and accuracy. Any mistakes in the diagnosis or AI model-building stage could have significant consequences for patients, treatment plans, and healthcare providers. This guide is for medical machine learning (ML), data operations (DataOps), and annotation teams and leaders wanting to learn more about how they can apply image segmentation for their computer vision projects. {{gray_callout_start}} Read more: Encord’s guide to medical imaging experiments and best practices for machine learning and computer vision. {{gray_callout_end}} Why is Medical Image Segmentation used In Healthcare Computer Vision Models?  Healthcare organizations, medical data operations, and ML teams can use medical image segmentation for dozens of computer vision use cases, including the following: Radiology Radiology is a medical field that generates an enormous amount of images (X-ray, mammography, CT, PET, and MRI), and healthcare organizations are increasingly turning to AI-based models to provide more accurate diagnoses at scale.  Training those models to spot what medical professionals can sometimes miss, or identify health issues more accurately, involves labeling and annotating vast datasets. Image segmentation is one way to achieve more accurate labels so that models can go into production faster, producing the results that healthcare organizations need.  Gastroenterology We can say the same about gastroenterology (GI) model development. Machine learning and computer vision models can be trained to more accurately identify cancerous polyps, ulcers, IBS, and other conditions at scale. Especially when it comes to outliers and edge cases that even the most skilled doctors and practice specialists can sometimes miss.  Histology Medical image annotation is equally useful for histology, especially when AI models can accurately apply widely-used staining protocols (including hematoxylin and eosin stain (H&E), KI67, and HER2). Image segmentation helps medical ML teams train algorithmic models, implement labeling at scale, and generate more accurate histology diagnoses from image-based datasets.  {{try_encord_today_bar}} ‍Ultrasound Image segmentation can help medical professionals more accurately label ultrasound images to identify gallbladder stones, fetal deformation, and other insights.  ‍Cancer Detection When cancerous cells are more difficult to detect, or the results from scans are unclear, computer vision models can play a role in the diagnosis process. Image segmentation techniques can be used to train computer vision models to screen for the most common cancers automatically, medical teams can make improvements in detection and treatment plans.  {{gray_callout_start}} Looking for a dataset to start training a computer vision model on? Here are the top 10 free, open-source healthcare datasets. {{gray_callout_end}} Different Ways to Apply Medical Image Segmentation In Practice   In this section, we’ll briefly cover 8 types of segmentation modes you can use for medical imaging. Here we’ll give you more details on the following types of image segmentation methods:  Instance segmentation  Semantic segmentation  Panoptic segmentation  Thresholding  Region-based segmentation  Edge-based segmentation  Clustering segmentation  Foundation Model segmentation For more information, check out our in-depth image segmentation guide for computer vision that also includes a number of deep learning techniques and networks.  {{try_encord}} Instance segmentation  Similar to object detection, instance segmentation involves detecting, labeling, and segmenting every object in an image. This way, you’re segmenting an object’s boundaries, and whether you’re doing this manually or AI-enabled, overlapping objects can be separated too. It’s a useful approach when individual objects need to be identified and tracked.  Semantic Segmentation  Semantic segmentation is the act of labeling every pixel in an image. This will provide a densely labeled image, and then an AI-assisted labeling tool can take these inputs and generate a segmentation map where pixel values (0,1,...255) are transformed into class labels (0,1,...n). Panoptic Segmentation  Panoptic is a mix of the two approaches outlined above, semantic and instance. Every pixel is applied a class label to identify every object in an image. This method provides an enormous amount of granularity and can be useful in medical imaging for computer vision where attention to detail is mission-critical.  Thresholding Segmentation  Thresholding is a fairly simple image segmentation method whereby pixels are divided into classes using a histogram intensity that’s aligned to a fixed value or threshold. When images are low-noise, threshold values can stay constant. Whereas in noisy images, a dynamic approach for setting the threshold is more effective.  In most cases, a greyscale image is divided into two segments based on their relationship to the threshold value. Two of the most common approaches to thresholding are global and adaptive.  Global thresholding for image segmentation divides images into foreground and background regions, with a threshold value to separate the two.  Adaptive thresholding divides the foreground and background using locally-applied threshold values that are contingent on image characteristics.  Region-based Segmentation  Region-based segmentation divides images into regions with similar criteria, such as color, texture, or intensity, using a method that involves grouping pixels. With this data, regions or clusters are then split or merged until a level of segmentation is achieved.  Annotators and AI-based tools can do this using a common split and merge technique or graph-based segmentation.  Edge-based Segmentation  Edge-based segmentation is used to identify and separate the edges of an image from the background. AI tools can be applied to detect changes in intensity or color values and use this to mark the boundaries of objects in images.  One method is the Canny edge detection approach, whereby a Gaussian filter is applied, applying non-maximum suppression to thin the edges and using hysteresis thresholding to remove weak edges.  Another method, known as Sobel, involves computing the gradient magnitude and direction of an image using a Sobel operator, which is a convolution kernel that extracts horizontal and vertical edge information separately. Clustering Segmentation  Clustering is a popular technique that involves grouping pixels into clusters based on similarities, with each cluster representing a segment. Different methods can be used, such as K-mean clustering, mean-shift clustering, hierarchical clustering, and fuzzy clustering. Visual Foundation Model Segmentation: (SAM) Segment Anything Model Meta’s Visual Foundation Model (VFM), called the Segment Anything Model (SAM), is a powerful open-source VFM with auto-segmentation workflows, and it’s live in Encord!  It’s considered the first foundation model for image segmentation, developed using the largest image segmentation known, with over 1 billion segmentation masks. Medical image annotation teams can train it to respond with a segmentation mask for any prompt.  Prompts can be asking for anything from foreground/background points, a rough box or mask, freeform text, or general information indicating what to segment in an image.  {{gray_callout_start}} Here’s how to use SAM to Automate Data Labeling in Encord. {{gray_callout_end}} How to Implement Medical Image Segmentation for Healthcare Computer Vision with Encord  With an AI-powered annotation platform, such as Encord, you can apply medical image segmentation more effectively, ensuring seamless collaboration between annotation teams, medical professionals, and machine learning engineers. At Encord, we have developed our medical imaging dataset annotation software in collaboration with data operations, machine learning, and AI leaders across the medical industry – this has enabled us to build a powerful, automated image annotation suite, allowing for fully auditable data and powerful labeling protocols. A few of the successes achieved by the medical teams we work with:  Stanford Medicine cut experiment duration time from 21 to 4 days while processing 3x the number of images in 1 platform rather than 3  King’s College London achieved a 6.4x average increase in labeling efficiency for GI videos, automating 97% of the labels and allowing their annotators to spend time on value-add tasks Memorial Sloan Kettering Cancer Center built 1000, 100% auditable custom label configurations for its pulmonary thrombosis projects Floy, an AI company that helps radiologists detect critical incidental findings in medical images, reduces CT & MRI Annotation time with AI-assisted labeling RapidAI reduced MRI and CT Annotation time by 70% using Encord for AI-assisted labeling.  Ready to automate and improve the quality, speed, and accuracy of your medical imaging segmentation?  Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams.  AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today.  Want to stay updated? Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning. Join our Discord channel to chat and connect.

sampleImage_best-ecg-annotation-tools

Machine learning has made waves within the medical community and healthcare industry. Artificial Intelligence (AI) has proven itself useful in numerous uses across a variety of domains, from Radiology and Gastroenterology to Histology and Surgery.  The frontier has now hit Electrocardiography (ECG) as well. With an annotation tool, you can annotate the different waves on your Electrocardiogram diagrams and train machine learning models to recognize patterns in the data. {{try_encord}} The first open-source frameworks have been developed to build models based on ECG data e.g. Deep-Learning Based ECG Annotation. In this example, the author automated the process of annotating peaks of ECG waveforms using a recurrent neural network in Keras. Even though the model was not 100% performant (it struggles to get the input/output right). It seems to work well on the QT database of PhysioNet. The Authors does mention it fails in some cases that it has never seen. Potential future development of machine learning would be to play with augmenting the ECGs themselves or create synthetic data. The 3 main components of an ECG: the P wave, which represents the depolarization of the atria; the QRS complex represents the depolarization of the ventricles; and the T wave, which represents the repolarization of the ventricles. Source: Wikipedia Another example of how deep learning and machine learning is useful in ECG waveforms can be found in the MathWorks Waveform Segmentation guide. Using a Long Short-Term Memory (LSTM) network, MathWorks achieved impressive results as seen in the confusion matrix below: If you want to get started yourself you can find a lot of open-source ECG datasets, e.g. the QT dataset from PhysioNet. Why are ECG Annotations Important in Medical Research? ECG annotation is an essential aspect of medical research and diagnosis, involving the identification and interpretation of different features in the ECG waveform. It plays a critical role in the accurate diagnosis and treatment of heart conditions and abnormalities, allowing you to detect a wide range of heart conditions, including arrhythmias, ischemia, and hypertrophy. Through the meticulous analysis of the ECG waveform, experts can identify any irregularities in the electrical activity of the heart, accurately determining the underlying cause of a patient's symptoms. The information gleaned from ECG annotation provides vital indicators of heart health, including heart rate, rhythm, and electrical activity. Regular ECG monitoring is invaluable in the management of patients with chronic heart conditions such as atrial fibrillation or heart failure. Here ECG annotation assists experts in identifying changes in heart rhythm or other abnormalities that may indicate a need for treatment adjustment or further diagnostic testing. With regular ECG monitoring and annotation, clinicians can deliver personalized care, tailoring interventions to the unique needs of each patient. {{medical_CTA_light}} How can Machine Learning Support ECG Annotations? Machine learning has significant potential in supporting and automating the analysis of ECG waveforms, providing a powerful tool for clinicians for improving the accuracy and efficiency of ECG interpretation.  By utilizing machine learning algorithms, ECG waveforms can be automatically analyzed and annotated, assisting clinicians in detecting and diagnosing heart conditions and abnormalities faster and at higher accuracy. One of the main benefits of machine learning in ECG analysis is the ability to process vast amounts of patient data. By analyzing large datasets, machine learning algorithms can identify patterns and correlations that may be difficult or impossible for humans to detect. This can assist in the identification of complex arrhythmias or other subtle changes in the ECG waveform that may indicate underlying heart conditions. Additionally, machine learning algorithms can help in the detection of abnormalities or changes in the ECG waveform over time, facilitating the early identification of chronic heart conditions. By comparing ECG waveforms from different time points, machine learning algorithms can detect changes in heart rate, rhythm, or other features that may indicate a need for treatment adjustment or further diagnostic testing. Lastly, machine learning models can be trained to recognize patterns in ECG waveforms that may indicate specific heart conditions or abnormalities. For example, an algorithm could be trained to identify patterns that indicate an increased risk of a heart attack or other acute cardiac event. By analyzing ECG waveforms and alerting clinicians to these patterns, it can help in the early identification and treatment of these conditions, potentially saving lives. The three tools we will be reviewing today are: Encord ECG OHIT ECG Viewer WaveformECG Encord ECG Encord is an automated and collaborative annotation platform for medical companies looking at ECG Annotation, DICOM/NIfTI annotation, video annotation, and dataset management. It's the best option for teams that are: Looking for automated, semi-automated or AI-assisted image and video annotation. Annotating all ontologies. Working with other medical modalities such as DICOM and NIfTI. Wanting one place to easily manage annotators, track performance, and create QA/QC workflows. Benefits & Key features: Use-case-centric annotations — from native DICOM & NIfTI annotations for medical imaging to ECG Annotation tool for ECG Waveforms. Allows for point and time interval annotations. Supports the Bioportal Ontology such as PR and QT intervals. Integrated data labeling services. Integrated MLOps workflow for computer vision and machine learning teams. Easy collaboration, annotator management, and QA workflows — to track annotator performance and increase label quality. Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for teams who: Are graduating from an in-house solution or open-source tool and need a robust, secure, and collaborative platform to scale their annotation workflows. Haven't found an annotation platform that can actually support their use case as well as they'd like (such as building complex nested ontologies, or rendering ECG waveforms). Team looking to build artificial neural networks for the healthcare industry. AI-focused cardiology start-ups or mature companies looking to expand their machine-learning practices should consider the Encord tool. Pricing: Free trial model, and simple per-user pricing after that. {{medical_CTA_light}} OHIF ECG Viewer The OHIF ECG Viewer provides can be found from Radical Imaging’s Github. The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. For a cardiologist just getting started this is a great tool and provides a baseline for comparing to other tools. Benefits & Key features: Leader in open-source software. Renders ECG waveform natively. Easy (& free) to get started labeling images with. Great for manual ECG annotation. Best for: Teams just getting started. Pricing: Free. WaveformECG The WaveformECG tool is a web-based tool for managing and analyzing ECG data. The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. So if you're new to the deep learning approach to ECG annotations the WaveformECG tool might be useful but if you’re looking at more advanced artificial neural networks or deep neural networks it might not be the best place. Benefits & Key features: Allows for point and time interval annotations and citations. Supports the Bioportal Ontology and metrics. Annotations are stored with the waveforms, ready for data analysis. Renders ECG waveform natively. Supports scrolling through each ECG waveform. Best for: Researchers and students. Pricing: Free. Conclusion There you have it! The 3 Best ECG annotation Tools for machine learning in 2023.  We’re super excited to see the frontier being pushed on ECG waveforms in machine learning and proud to be part of the journey with our customers. If you’re looking into augmenting the ECGs themselves or creating synthetic data get in touch and we can provide you input and help with it!

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Computer Science > Computer Vision and Pattern Recognition

Title: annotheia: a semi-automatic annotation toolkit for audio-visual speech technologies.

Abstract: More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most studies are mainly benchmarked on English. This situation is aggravated when tasks involving both acoustic and visual speech modalities are addressed. In order to promote research on low-resource languages for audio-visual speech technologies, we present AnnoTheia, a semi-automatic annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. In addition, to show the complete process of preparing AnnoTheia for a language of interest, we also describe the adaptation of a pre-trained model for active speaker detection to Spanish, using a database not initially conceived for this type of task. The AnnoTheia toolkit, tutorials, and pre-trained models are available on GitHub.

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  1. 21 Best Annotation Tools For Documents, Video, And More

    Here's a list of the seven most common types of annotation tools: Data annotation tools - these tools allow users to highlight, underline, strikethrough, or add comments to text-based content Image annotation tools - annotate images by adding text, shapes, or drawings

  2. Top 8 Annotation Tools (Features, Pros, Cons, Pricing)

    Top 8 Annotation Tools in 2022 1. ClickUp 2. Filestage 3. Prodigy 4. Annotate 5. PDF Annotator 6. Drawboard Projects 7. Doccano 8. lnk2Go Bonus Tool: Markup Hero Conclusion Looking for the best annotation software?

  3. Data Annotation Tools for Machine Learning (Evolving Guide)

    Introduction: Will this guide be helpful to me? The Basics: Data Annotation Tools and Machine Learning A Critical Choice: Build vs. Buy How to Choose a Data Annotation Tool The Best Data Annotation Tools: Commercial and Open Source Iteration & Evolution: Changing Data Annotation Needs, New Tools Questions to Ask Your Data Annotation Tool Provider

  4. 13 Best Image Annotation Tools of 2023 [Reviewed]

    8 min read · February 2, 2023 Alberto Rizzoli V7 V7 Labelbox Scale AI SuperAnnotate Dataloop Playment Supervise.ly Hive Data CVAT LabelMe LabeIimg VoTT Img Lab How to choose the best image annotation tool Text Link Let's face it— It's easy to get confused when choosing the best image annotation tool for your specific use case.

  5. 13 Absolutely Best Annotation Tools to Mark Up Your Files

    #1. Functionality #2. Annotation capabilities #3. Efficiency 13 Best Annotation Tools for 2023 #1. CELUM #2. Filestage #3. Annotate #4. Doccano #5. Ink2Go #6. PDF Annotator #7. Markup Hero #8. Zoho Annotator #9. ClickUp #10. GoVisually #11. Diigo

  6. 25 Best Annotation Software Tools (Free & Paid Apps

    💡 KEY INSIGHTS Flexibility in annotation tools is crucial for adapting to various data types and annotation needs. Advanced annotation tools significantly reduce the time required for data labeling through automation features. Ensuring data privacy and security is a paramount consideration when choosing annotation tools for sensitive projects.

  7. Top 8 Annotation Tools To Mark Up Documents in 2023

    Sound familiar? That's where annotation tools can help. With the correct software, you can seamlessly annotate multiple file types and collect feedback from everyone on your team. Collaborating, annotating, and proofing your content is an important process for many businesses today.

  8. Deep Learning Guide: Choosing Your Data Annotation Tool

    VIA is another tool for image annotations that should be on your watch list. It's a free, open-source solution developed by a team from Oxford University. In contrast to LabelImg, VGG Image Annotator runs entirely in a browser window. Even though it's a web-based app, users can work offline in most web browsers.

  9. Top 11 annotation tools: features & pricing breakdown (2024)

    1. MarkUp.io 2. Pastel 3. Zoho Annotator 4. Markup Hero 5. GoVisually 6. Prodigy 7. Filestage 8. Frame.io 9. Annotate 10. Bubbles 11. Hypothes.is Frequently asked questions (FAQs) What is an annotation tool?

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    An application that helps machine learning teams facilitating the creation and management of annotations. Apache-2. DataGym. Open source annotation and labeling tool for image and video assets. MIT. Diffgram. Training Data (Data Labeling, Annotation, Workflow) for all Data Types (Image, Video, 3D, Text, Geo, Audio, more) at scale. ELv2.

  11. The Ultimate Guide Of Best Open Source Annotation Tools 2023

    The Computer Vision Annotation Tool is a powerful and efficient image and video annotator. It is open source and web based, and though its user interface is not very intuitive, amateurs and professionals will be able to take advantage of it after getting over the learning curvet. Key Features: Web based. Maintained by Intel.

  12. GitHub

    A curated list of awesome data labeling tools. Images. labelImg - LabelImg is a graphical image annotation tool and label object bounding boxes in images; CVAT - Powerful and efficient Computer Vision Annotion Tool; labelme - Image Polygonal Annotation with Python; VoTT - An open source annotation and labeling tool for image and video assets

  13. The Ultimate Guide to Text Annotation: Techniques, Tools, and Best

    1. What is Text Annotation? Text annotation is a crucial process that involves adding labels, comments, or metadata to textual data to facilitate machine learning algorithms' understanding and analysis.

  14. 10 of the best open-source annotation tools for computer vision

    1. Label Studio Image source Our absolute favorite open source labeling tool is Label Studio by Heartex Inc. We recently featured the enterprise version of the tool in our previous article because we were impressed by its versatility and advanced functionalities for active learning and collaboration.

  15. Dataset list

    A list of the best open-source annotation tools for labeling data. Use this form to add new tools to the list. Subscribe to get updates when new datasets and tools are released.

  16. The Ultimate Guide to Image Annotation: Techniques, Tools, and Best

    1. Interpolation: Video annotation tools allow for the annotation of keyframes at the beginning and end of a video sequence. The tool can then interpolate annotations for the frames in between, saving time and effort. 2. Temporal Context: Videos provide temporal context, allowing AI models to understand object movements and changes over time ...

  17. List of manual image annotation tools

    JavaScript, HTML, CSS, Python, Django. MIT License. [1] [2] [3] LabelMe. Online annotation tool to build image databases for computer vision research. Perl, JavaScript, HTML, CSS [4] MIT License. TagLab. Desktop open source interactive software system for facilitating the precise annotation of benthic species in orthophoto of the bottom of the sea.

  18. The 15 Best Annotation Tools for Client Feedback

    Markup Hero is a sophisticated highlighting and markup web annotation tool for project managers, product managers, academics, and writers. It is available for Windows, Linux, Chrome, and Mac devices. Use Markup Hero to upload images and PDFs, capture scrolling websites, and pin annotations to uploaded documents.

  19. List of Open-Source Annotation Tools for Machine Learning Research

    simple annotation tool to label waveforms. contains some processing capabilities as well. audino. transcription and labeling for audio data. audio-annotator. webinterface for audio annotation. audio-labeler. web-based annotation tools for waveforms. EchoML. web-based annotation tool for audio files.

  20. GitHub

    A curated list of awesome annotation tools. Please feel free to pull requests to add links. Text. Caution: This is based on my survey about a year ago. I will update it asap. Supporting Tasks. Classification Sequence Labeling Seq2seq Relation Dictionary Choice; doccano:

  21. 7+ Best Annotation Tools of 2024 (FREE and PAID)

    The 8 best annotation tools are Adobe Acrobat Pro DC, Markup Hero, Annotate, Filestage, zipBoard , ClickUp, PDF Annotator, and Hive. Adobe Acrobat Pro DC 14.99$ monthly 1 User Free 7-day trial Best for individuals and businesses Cloud GO TO SITE Markup Hero $4 monthly 1 User Free version Best for freelancers and businesses Web, Windows, and Mac

  22. Ultimate list of free and paid best image annotation tools in 2023

    3. Labelme: Labelme is an open source annotation tool that you could use to process image datasets. A key feature to be noted on the tool is the "File List" option on the bottom right. This could come in handy when you have a lot of images to annotate, because it reduces the chances of you missing any images.

  23. Best DICOM Labeling Tools [2024 Review]

    Hive: Cloud-based AI tools for organizations that need to apply labels across a wide range of data types. Dataloop: Software to train and improve ML and AI models with extensive annotation capabilities. Appen: One of the oldest labeling services platforms on the market, launched in 1996.

  24. AnnoTheia: A Semi-Automatic Annotation Toolkit for Audio-Visual Speech

    More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most studies are mainly benchmarked on ...