16+ Best Data Annotation Tools for 2025: Features, Benefits, and More

Data annotation is essential for training machine learning models. It involves labeling raw data so that AI systems can learn from it and make accurate predictions. In fact, the data collection and labeling market is expected to grow at a CAGR 23.7% (2024–2032), emphasizing the need for efficient tools.

This blog will help you choose the right data annotation tool for your needs that significantly improve your workflow efficiency and collaboration.

16+ Top Data Annotation Tools to Check Out in 2025

When you’re working on complex machine learning or AI projects, having the right data annotation tool is crucial to keep everything organized and efficient. Data annotation tools ensure that data is structured, labeled, and categorized accurately to improve model performance. 

Now that you know what to look for, let's dive into the top data annotation tools you should explore in 2025.

1. Prodigy

Source: Prodigy

Prodigy is a powerful, flexible data annotation tool. It is designed for machine learning teams to annotate data in a way that aligns with their model’s needs.

• Overview: Prodigy is a human-in-the-loop annotation tool, focusing on active learning.
• Purpose: It is used to quickly label data and improve machine learning models.
• Users: Data scientists, machine learning engineers, AI researchers.

Use Cases and Examples

• Prodigy is used to label medical images, aiding in early disease detection like cancer in X-rays and MRIs for accurate diagnoses.
• It is applied to text annotation for sentiment analysis on social media or named entity recognition in financial reports for automated data extraction.

Key Features

• Active learning integration to optimize the labeling process.
• Highly customizable to fit specific use cases.
• Real-time model improvement during the annotation process.

Benefits and Challenges

• Benefits: Fast data annotation, allows integration with machine learning pipelines.
• Challenges: Requires some technical expertise to get the most out of it.

Now that we've explored Prodigy, let's dive into Labelbox, which takes a different approach to annotation with its enterprise-grade features.

2. Labelbox

Source: Labelbox

Labelbox is an enterprise-grade data annotation platform designed to scale data labeling efforts. Its collaborative features make it ideal for teams.

• Overview: Labelbox allows users to create and manage data annotations across multiple projects.
• Purpose: Streamlines data labeling workflows and accelerates machine learning model development.
• Users: Enterprises, machine learning teams, data scientists.

Use Cases and Examples

• Self-Driving Cars: Labelbox helps autonomous vehicle companies like Waymo label images for object detection, identifying pedestrians, traffic lights, and road signs for model training.
• Document Classification and Named Entity Recognition (NER): Prodigy is used by legal and financial firms to annotate contracts, training models for document classification and extracting key information like dates and clauses.

Key Features

• A user-friendly interface with pre-built templates.
• Tools for collaborative data labeling with feedback loops.
• API integration for automation and scale.

Benefits and Challenges

• Benefits: Great for large-scale annotations, easy collaboration among team members.
• Challenges: Cost can be prohibitive for smaller teams.

Also Read: How to Implement Machine Learning Steps: A Complete Guide

Now, let’s explore another powerful tool, Amazon SageMaker Ground Truth, which excels in integrating with AWS services.

3. Amazon SageMaker Ground Truth

Source: Amazon AWS

SageMaker Ground Truth from AWS is a robust tool for labeling data to build highly accurate machine learning models.

• Overview: A fully managed data labeling service integrated with AWS machine learning ecosystem.
• Purpose: Streamline the data labeling process for machine learning projects.
• Users: Data scientists, machine learning engineers, cloud developers.

Use Cases and Examples

• Can be used for annotating data for computer vision tasks like object detection in satellite imagery.
• Suitable for text annotation in NLP tasks like machine translation.

Key Features

• Built-in human labeling workforce for verification.
• Integration with AWS machine learning tools for model building.
• Active learning to improve accuracy over time.

Benefits and Challenges

• Benefits: Scalability, tight integration with AWS services.
• Challenges: Limited to AWS ecosystem, might be complex for beginners.

After exploring Amazon SageMaker Ground Truth, let's now look at another powerful tool, Supervisely, for visual data annotation.

4. Supervisely

Source: Supervisely

Supervisely is an AI-powered platform designed to simplify and accelerate the annotation process, especially for images and video.

• Overview: A data annotation platform primarily focused on visual data like images and videos.
• Purpose: Helps create high-quality labeled data for computer vision models.
• Users: AI developers, machine learning engineers, visual data analysts.

Use Cases and Examples

• Used for image segmentation tasks in autonomous vehicles.
• Annotating medical images to detect tumors or anomalies in X-rays.

Key Features

• Extensive support for 2D and 3D data annotation.
• Collaborative tools for team-based annotation projects.
• Advanced automation features using AI-powered tools.

Benefits and Challenges

• Benefits: Excellent for computer vision tasks, automated features.
• Challenges: Supervisely's complexity, especially in 3D annotation setup and AI model training, can overwhelm non-technical users, increasing the learning curve and setup difficulty.

Also Read: Explanatory Guide to Clustering in Data Mining – Definition, Applications & Algorithms

Now, let's look at another tool that excels in simplicity and is ideal for smaller projects: VGG Image Annotator (VIA).

5. VGG Image Annotator (VIA)

Source: Github

VGG Image Annotator (VIA) is a simple and open-source tool for image annotation. It’s lightweight and ideal for smaller projects.

• Overview: A simple, free-to-use tool for image and video annotation, designed with data annotation and machine learning in mind.
• Purpose: Primarily used for labeling images with bounding boxes, polygons, and other basic features.
• Users: Academic researchers, students, small teams.

Use Cases and Examples

• Annotating images for classification tasks in machine learning projects.
• Used by research teams to label datasets for facial recognition.

Key Features

• Web-based tool with no installation required.
• Supports multiple annotation types (bounding boxes, polygons, points).
• Exportable in standard formats like COCO, Pascal VOC.

Benefits and Challenges

• Benefits: Easy to use, free and open-source, lightweight.
• Challenges: Limited features compared to larger tools.

Also Read: Clustering vs Classification: Difference Between Clustering & Classification

Dataturks specializes in both text and image annotation, making it versatile for a range of machine learning tasks.

6. Dataturks

Source: Github

Dataturks is a data labeling tool that focuses on both image and text annotation. It provides a streamlined interface for data scientists and machine learning teams.

• Overview: Provides support for a variety of annotation types including text and images.
• Purpose: Efficient data annotation tool designed to cater to different ML tasks.
• Users: Machine learning teams, data scientists, enterprises.

Use Cases and Examples

• Used for labeling text data for sentiment analysis or text classification.
• Helps annotate images for object detection tasks.

Key Features

• Customizable labeling tasks for various types of data.
• Supports collaborative projects.
• Integration with other machine learning tools.

Benefits and Challenges

• Benefits: Versatile, easy integration with ML workflows.
• Challenges: May not be suitable for large-scale projects without extra customization.

After exploring Dataturks, let's now look at Dataloop, a tool that offers more automation in the annotation process.

7. Dataloop

Source: Dataloops

Dataloop is an AI-powered data annotation platform that enhances productivity by automating parts of the annotation process. This platform significantly reduces manual effort and accelerates the labeling process by incorporating machine learning algorithms and deep learning models to assist in annotating large datasets.

• Overview: Dataloop’s platform helps teams automate data labeling and manage large datasets.
• Purpose: Optimize the process of creating labeled data for machine learning applications.
• Users: AI and machine learning teams, data managers, and enterprise businesses.

Use Cases and Examples

• Annotating large volumes of images for object detection in smart security systems.
• Text annotation for social media sentiment analysis.

Key Features

• AI-assisted data labeling.
• Built-in project management tools for seamless collaboration.
• Real-time feedback during labeling.

Benefits and Challenges

• Benefits: Automation increases speed, reduces human error.
• Challenges: Advanced features may require technical knowledge.

As we explore more tools, Tesseract offers a unique approach to data annotation, especially for text extraction tasks.

8. Tesseract

Source: Github

Tesseract is an open-source tool that specializes in optical character recognition (OCR). It helps label text within images, converting it into structured data.

• Overview: A robust OCR tool used primarily for extracting text from images.
• Purpose: Converts images with text into machine-readable formats.
• Users: AI researchers, data scientists, businesses using text-based data from images.

Use Cases and Examples

• Extracting text from scanned documents and invoices for automation.
• OCR for extracting handwritten text in medical records.

Key Features

• Open-source and free to use.
• High accuracy in text extraction from various languages.
• Multi-platform support.

Benefits and Challenges

• Benefits: Open-source, highly accurate, flexible.
• Challenges: May require customization for complex use cases.

Also Read: Top 10 Big Data Tools You Need to Know To Boost Your Data Skills in 2025

After exploring Tesseract’s capabilities, let’s now dive into Snorkel’s unique approach to automating data annotation.

9. Snorkel

Source: Snorkel

Snorkel is an innovative tool that focuses on weak supervision, using noisy labels to enhance model accuracy.

• Overview: Snorkel helps to create labeled datasets by leveraging weak supervision and user-defined labeling functions.
• Purpose: Generate high-quality labeled datasets with less human input.
• Users: Researchers, machine learning teams, AI engineers.

Use Cases and Examples

• Labeling datasets where human labeling is expensive or time-consuming, like medical records.
• Generating weak labels for NLP tasks like spam detection.

Key Features

• Ability to work with noisy or incomplete data.
• Automates much of the labeling process.
• Integrates with other machine learning tools.

Benefits and Challenges

• Benefits: Great for noisy data, reduces labeling cost.
• Challenges: Requires significant understanding of weak supervision.

Also Read: Types of Machine Learning Algorithms with Use Cases Examples

As you explore automation and advanced features, let’s now look at a tool focused on image annotation: RectLabel.

10. RectLabel

Source: ReactLabel

RectLabel is a versatile image and video annotation tool that supports bounding boxes, polygons, and other annotation types. It is widely used for visual data labeling.

• Overview: A powerful tool for annotating images and videos, designed with ease of use in mind.
• Purpose: Enables efficient labeling of visual data, especially for computer vision tasks.
• Users: Data scientists, AI engineers, and research teams.

Use Cases and Examples

• Annotating images for object detection, such as identifying vehicles in traffic surveillance footage.
• Labeling video frames to track movements in sports analytics or surveillance applications.

Key Features

• Easy-to-use interface for manual and automated annotations.
• Support for multi-frame video annotation and exporting to different formats.
• Integration with machine learning frameworks for seamless workflow.

Benefits and Challenges

• Benefits: User-friendly, versatile annotation tools for various types of visual data.
• Challenges: Lacks some advanced features found in larger enterprise-grade tools.

Also Read: Linear Regression in Machine Learning: Everything You Need to Know

Moving from RectLabel, CVAT offers additional features for more advanced image and video annotation needs.

11. CVAT

Source: CVAT

CVAT (Computer Vision Annotation Tool) is an open-source tool developed by Intel for annotating images and videos. It is commonly used for tasks involving object detection, image segmentation, and more.

• Overview: A widely used open-source tool designed for computer vision data labeling.
• Purpose: Helps teams efficiently label data for computer vision models, especially in large datasets.
• Users: AI teams, researchers, and organizations working with computer vision data.

Use Cases and Examples

• Labeling images for autonomous vehicle development, such as marking pedestrians or traffic signals.
• Annotations for facial recognition tasks, such as identifying and tracking faces in security footage.

Key Features

• Supports both manual and automatic annotation methods.
• Collaborative features for team-based annotation.
• Extensive support for different annotation formats and export options.

Benefits and Challenges

• Benefits: Free, open-source, highly customizable.
• Challenges: May require technical knowledge to set up and use effectively.

Also Read: Face Recognition using Machine Learning: Complete Process, Advantages & Concerns in 2025

While CVAT excels in visual data, LightTag specializes in text annotation for NLP tasks.

12. LightTag

Source: ACL Anthology

LightTag is a text annotation tool that emphasizes speed and ease of use, allowing users to efficiently label text data for machine learning tasks, particularly in the realm of Data Annotation and Machine Learning.

• Overview: A platform dedicated to annotating text with an easy-to-use interface and streamlined workflow.
• Purpose: Helps speed up the process of labeling text for machine learning tasks, particularly NLP applications.
• Users: NLP practitioners, data scientists, research teams.

Use Cases and Examples

• Used for text classification tasks like categorizing news articles or emails.
• Annotating medical text for extracting critical information, such as patient histories or diagnostic results.

Key Features

• Collaborative features that allow multiple users to work on the same data.
• Real-time feedback and progress tracking.
• Customizable labeling interface to suit specific projects.

Benefits and Challenges

• Benefits: Fast, intuitive user interface, great for collaborative work.
• Challenges: Limited support for non-textual data, such as images or videos.

Also Read: Bias vs Variance in Machine Learning: Difference Between Bias and Variance

Moving from a powerful text annotation tool lightweight to LabelImg offers a simple approach to image annotation.

13. LabelImg

Source: Viso AI

LabelImg is an open-source graphical image annotation tool that focuses on creating bounding boxes for object detection tasks. It’s particularly suitable for simple projects.

• Overview: A lightweight image annotation tool for object detection tasks.
• Purpose: Designed to quickly and efficiently label objects within images for computer vision models.
• Users: Computer vision researchers, AI engineers, and students.

Use Cases and Examples

• Used for labeling objects in retail environments, such as identifying products on shelves for an AI-based inventory management system.
• Annotating wildlife images for animal detection in conservation projects.

Key Features

• Simple interface for creating bounding boxes and exporting labels.
• Open-source and free to use.
• Support for multiple file formats (XML, CSV, etc.).

Benefits and Challenges

• Benefits: Free, simple to use, no setup required.
• Challenges: Lacks advanced features for more complex annotation tasks.

Also Read: Introduction to Classification Algorithm: Concepts & Various Types

After exploring LabelImg's features, it's important to consider other automated annotation tools like InnoMatics for further efficiency.

14. InnoMatics

Source: Innomatics

InnoMatics is an automated data annotation tool designed to streamline the process by leveraging machine learning models to assist in annotating data.

• Overview: A tool that automates the annotation process by using pre-trained models to generate initial labels.
• Purpose: Accelerates data labeling by combining human input with machine learning.
• Users: AI researchers, data scientists, enterprises working with large datasets.

Use Cases and Examples

• Annotating large video datasets for object detection in security applications.
• Image classification for medical research, such as identifying certain diseases in X-ray images.

Key Features

• Automated labeling powered by pre-trained models.
• Customizable workflows to adjust for different project needs.
• Integration with other machine learning tools for further model training.

Benefits and Challenges

• Benefits: Reduces time and effort by automating large parts of the process.
• Challenges: The initial machine-generated labels may require human validation, adding some extra effort.

Also Read: Regularization in Machine Learning: How to Avoid Overfitting?

InnoMatics automates much of the annotation process, reducing manual effort. Now, let’s explore how Clarity AI enhances labeled data accuracy.

15. Clarity AI

Source: Clarity AI

Clarity AI is a data annotation tool used for creating structured datasets from unstructured data. It is designed for AI projects requiring highly accurate and labeled data.

• Overview: A tool that specializes in creating labeled datasets for AI and machine learning.
• Purpose: Helps turn unstructured data into usable, structured formats for model training.
• Users: AI developers, data scientists, business intelligence professionals.

Use Cases and Examples

• Labeling consumer reviews for sentiment analysis.
• Annotating large volumes of text or images in the fashion industry for style recognition.

Key Features

• Supports both text and visual data annotation.
• Built-in tools for data quality checks and automated labeling.
• Scalable, ideal for large projects.

Benefits and Challenges

• Benefits: High accuracy, scalable solution.
• Challenges: May require training for teams to utilize effectively.

From AI-powered solutions like Clarity AI, we explore how Scale AI takes enterprise-level projects to new heights.

16. Scale AI

Source: Scale AI

Scale AI is a powerful data annotation platform used for large-scale machine learning projects. It offers both manual and automated annotation capabilities.

• Overview: A robust platform offering services for computer vision, natural language processing, and more.
• Purpose: Helps manage large volumes of data annotation tasks with scalability.
• Users: Enterprises, machine learning teams, AI-driven startups.

Use Cases and Examples

• Annotating satellite images for land usage analysis.
• Labeling text data for entity extraction tasks in business intelligence applications.

Key Features

• High-quality annotation services with human-in-the-loop validation.
• AI-powered automation tools for faster labeling.
• API integration for seamless workflow.

Benefits and Challenges

• Benefits: Scalable, high-quality results.
• Challenges: Cost can be high for smaller teams or projects.

Also Read: Steps in Data Preprocessing: What You Need to Know?

Now, we transition to Mockaroo, which allows you to generate synthetic data for efficient machine learning training.

17. Mockaroo

Source: Mockaroo

Mockaroo is a data generation tool that supports annotation by creating realistic datasets that can be used for training machine learning models.

• Overview: A tool that helps generate mock data and creates custom datasets for machine learning projects.
• Purpose: Enables teams to generate labeled datasets quickly for model training, without needing access to real-world data.
• Users: Data scientists, AI teams, machine learning practitioners.

Use Cases and Examples

• Used for generating synthetic data in areas like finance, where real data may be difficult to access.
• Generating mock data for testing and training in projects related to e-commerce or healthcare.

Key Features

• Allows users to customize data generation rules.
• Supports export to various file formats like CSV, JSON, SQL, and more.
• Flexible enough for use across different industries.

Benefits and Challenges

• Benefits: Fast generation of realistic data, no real-world data needed.
• Challenges: May lack the authenticity of actual datasets, limiting use in certain applications.

Also Read: Top 30 Machine Learning Skills for ML Engineer in 2024

At last, let's explore Kili, an advanced tool combining deep learning with human input for high-quality data labeling.

 18. Kili

Source: Kili

Kili is an advanced data annotation platform that specializes in geospatial data, integrating robust labeling features with cutting-edge technology for enhanced accuracy and efficiency.

• Overview: Kili is a data annotation tool designed to handle large-scale geospatial and image-based datasets, offering specialized features for multi-layer annotation tasks.
• Purpose: The platform automates parts of the annotation process while ensuring high precision with human oversight, making it ideal for teams handling satellite imagery, urban planning, environmental monitoring, and other geospatial challenges.
• Users: AI teams, data scientists, geospatial researchers, and companies working with geospatial data at scale.

Use Cases and Examples

• Urban Planning: Kili is used for annotating satellite imagery to help with urban development projects. 
• Environmental Monitoring: Used by environmental scientists to annotate images from satellites to track changes in natural terrains, monitor forest cover, or observe ocean changes, providing valuable insights for sustainability projects.

Key Features

• Geospatial Data Support: Kili offers native support for geospatial data formats such as GeoTIFF, GeoJSON, and more.
• Collaborative Tools: The platform facilitates teamwork with collaborative annotation features, allowing teams to review, iterate, and validate labels in real-time.
• AI-Assisted Labeling: Built-in AI tools help automate repetitive tasks, such as object detection in images, reducing manual effort while maintaining high-quality results.

Benefits and Challenges

• Benefits: Kili improves the speed and accuracy of annotating large geospatial datasets, particularly useful in industries like agriculture, defense, and urban planning. 
• Challenges: While the platform is powerful, its setup for large-scale datasets can be complex, especially when dealing with massive amounts of geospatial data

Now that you know the top data annotation tools, let's explore how to choose the best one for your needs.

How to Choose the Best Data Annotation Tool for Your Project?

A suitable tool ensures that data labeling is accurate, efficient, and scalable. Selecting the wrong data annotation tool can result in inefficiency, inaccurate annotations, and wasted resources.

In 2025, data annotation tools have evolved to accommodate a wide variety of data types, use cases, and team dynamics. To help you make the right decision, here are some key factors to consider when choosing a tool.

1. Data Type Compatibility

One of the first things to evaluate when selecting a data annotation tool is whether it supports the type of data you are working with. Different tools specialize in different types of data, such as images, text, audio, or video.

For instance, if you are dealing with text-based data, you need a tool that supports text annotation for tasks like sentiment analysis or entity recognition. On the other hand, for image or video annotation, tools that support object detection or image segmentation would be more relevant.

• Image Annotation: Tools like Labelbox and Prodigy work well for image-based annotations, such as object detection and classification.
• Text Annotation: If you are working on natural language processing (NLP) tasks, tools like LightTag and Dataturks are optimized for text-based data.
• Audio/Video Annotation: Tools like Supervisely and CVAT are well-suited for annotating audio or video data.

Also Read: A Comprehensive Guide to Understanding the Different Types of Data

2. Scalability

Scalability is another critical factor to consider. As your project grows, you will likely need more annotations, often at a faster pace. A scalable tool can grow with your needs and handle larger datasets without compromising performance or quality.

• Example: For small-scale projects, tools like VGG Image Annotator (VIA) or LabelImg might suffice, but they could face limitations when scaling to enterprise-level data.

Also Read: Evaluation Metrics in Machine Learning: Top 10 Metrics You Should Know

3. User Interface

The user interface (UI) of a data annotation tool directly impacts how easy and efficient the annotation process will be. A well-designed interface allows users to quickly and accurately label data, which is essential for maintaining high productivity.

• Example: Tools like Prodigy and Labelbox are recognized for their clean, intuitive user interfaces that make annotation tasks straightforward.

4. Automation Features

Automation features can drastically improve the efficiency of the data annotation process, especially when dealing with large datasets. By using pre-trained machine learning models or semi-automated annotation features, you can reduce the need for manual labor and speed up your project.

• Example: Snorkel uses weak supervision and machine learning models to automate much of the annotation process.

5. Cost

Cost is an important consideration, especially for students or small teams with limited budgets. Different tools have different pricing models, from one-time fees to subscription-based services. It’s essential to weigh the tool’s capabilities against its price to ensure it fits within your budget.

• Example: Scale AI and Amazon SageMaker Ground Truth offer enterprise-level solutions but come with a higher price tag due to their advanced features and scalability.

6. Collaboration Capabilities

Collaboration is key to effective project management. If you are working in a team, you will need a tool that allows multiple people to annotate, review, and manage tasks together. Features like user access control, task assignment, and real-time collaboration can help teams stay organized and productive.

• Example: LightTag also includes real-time collaboration, which is especially useful for text annotation tasks in NLP projects.

7. Integration with Other Tools

Data annotation often needs to be integrated with other tools or systems, such as machine learning platforms, cloud storage, or project management tools. Seamless integration ensures that annotated data flows smoothly into the next phase of your project without additional manual effort.

• Example: Labelbox integrates with a wide range of machine learning platforms, enabling seamless model training with labeled data.

Also Read: Understanding 8 Types of Neural Networks in AI & Application

Now that you know how to choose the right tool, let's explore how upGrad can help you succeed in this field.

How Can upGrad Help You Launch a Career in Data Annotation and Machine Learning?

As you progress in machine learning and advanced fields, having the right tools and guidance is essential for understanding these concepts.

To support your growth, upGrad offers comprehensive machine learning courses and programs designed by industry experts. Their blend of theoretical and practical learning ensures you're ready to tackle challenges and drive innovation.

Here are some of the top relevant programs offered:

• Executive Diploma in Machine Learning and AI
• Post Graduate Certificate in Machine Learning and Deep Learning (Executive)
• Post Graduate Certificate in Machine Learning & NLP (Executive)
• Job-ready Program in Artificial Intelligence & Machine Learning
• Fundamentals of Deep Learning and Neural Networks
• Logistic Regression for Beginners
• Data Science in E-commerce

Confused about where to start? Book a one-on-one career counseling session with the experts today or visit any of upGrad’s offline centres to interact with mentors, attend live workshops, and immerse yourself in a valuable learning experience!

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References:
https://straitsresearch.com/report/data-collection-and-labeling-market