7 Data Annotation Tools for Better Machine Learning Outcomes

Top Data Annotation Tools Review & How to Enhance Machine Learning Outcomes with Human-in-the-Loop Approach

Annotating large datasets manually can be challenging, time & resource-consuming, and error-prone for businesses. It can also be subjective and inconsistent due to differences in annotators’ interpretations. But with the right data labeling tool, you can make the process cost-effective, efficient, and consistent.

These tools cater to specific data types (image, video, text, audio, spreadsheet, sensor) and offer various deployment options (on-premise, container, SaaS, Kubernetes).

To help you choose the right one for your project needs, here we are reviewing the seven best data annotation tools for different data types. As AI also has some limitations, we will also talk about the proven HITL (human-in-the-loop) approach to maximize the outcome efficiency of these tools.

How to Choose the Right Data Annotation Tool for Your Needs?

While plenty of open-source and commercial data labeling tools are available, not all can be suitable for your datasets. So, to pick the right labeling tool for your datasets, there are certain criteria you must consider:

• Annotation use cases

• Data types supported (text/image/video)

• Dataset Management (supports large datasets and formats or not)

• Annotation methods supported

• Deployment models (On-Premise, Cloud-based, Kubernetes, etc.)

• Pricing models

• User interface

• Data security features

• Data quality assurance

• Collaboration features

• Integration options

7 Best Data Annotation Tools (Free & Paid) – Detailed Review

Based on the above-mentioned criteria, We have reviewed these seven AI annotation tools (both free and paid) for different business and dataset types. Let’s see which one is the best pick for you.

1. CVAT

CVAT (Computer Vision Annotation Tool) is one of the most popular image and video annotation tools by Intel for small businesses, researchers, and students. It is open-source, free, and web-based.

Pros:

• Web-based, Free, and Collaborative
• Supports automatic annotation
• Employs interpolation between keyframes.

Cons:

• Limited Browser Support
• Lacks key security features like SSO (Single-Sign-On), Audit Trails, etc.

2. V7 Labs

V7 Labs is an automated annotation and data management tool supporting the HITL approach. It can annotate any visual data and supports autoML model training for automatic labeling. The auto-annotation functionality uses a deep learning model to segment items and automatically generates pixel-perfect polygon masks in seconds.

Pros:

• Automated annotation features can be used easily without prior technical training
• Composable workflows make multi-staging tasks simpler
• Supports versatile annotation options such as bounding boxes, key points, and semantic segmentation
• Customizable

Cons:

• Slightly expensive for small businesses

3. LabelBox

LabelBox is a popular vector annotation tool known for its speed and accuracy. The tool can be configured in minutes and is scalable for all team sizes to cater to different project needs. Along with an image labeling platform, it also provides annotation services for different business needs.

Pros:

• Allow building custom labeling interface
• Versatile and supports various data types
• Data-driven insights and live-project status updates
• Pre-labeling techniques improve annotation speed by 65% without affecting labeling qualities

Cons:

• Video frame labeling is tedious
• Technical support takes a long time

4. PDF Annotator

PDF Annotator is a simple, reliable and easy-to-use labeling tool for PDF documents. It allows you to add signatures, comments, images, links, markup designs, page numbers, etc., to your documents and free-hand annotation using its pen tool.

Pros:

• Features embedded image editor
• Can export documents in XLS, PPT and Doc format
• Allows you to delete, extract or move specific parts of a document
• 60-day money-back guarantee

Cons:

• Only support Windows
• Cannot open Adobe DRM protected documents

5. Scale AI

Scale AI is an advanced annotation tool that supports voluminous 3D sensor, image and video data for ML-powered labeling. Its automated quality assurance system and features like Superpixel segmentation makes it the best image annotation tool online.

Pros:

• Supports 3D Sensor Fusion annotation for RADAR and LiDAR applications
• Supports machine-learning algorithms and HITL approach for high-quality annotations
• Customizable workflows
• Subject matter expertise
• Real-time feedback

Cons:

• Expensive
• Don’t let organizations work with their annotators on the platform

6. Super Annotate

SuperAnnotate offers both annotation software and a platform for creating accurate training data across various data types. The tool features advanced machine learning algorithms to speed up the development of computer vision models by streamlining the annotation process.

Pros:

• Supports bounding boxes, pointers, polygons, lines, and segmentation annotation types
• Dedicated annotation project manager
• High-quality data with subject matter experts
• Robust and user-friendly

Cons:

• Lacks OCR functionality
• Limited 3D annotation capabilities

7. Doccano

Docanno is a popular open-source, free text annotation tool for sentiment analysis, sequence-to-sequence learning and sequence labeling. The tool features REST API, collaborative annotation features, mobile compatibility and multi-language support to create labeled data.

Pros:

• Can label text for any language
• Simple and user-friendly
• Open-source and free
• Supports text classification, entity recognition and text summarization

Cons:

• Heavy-coding setup requires technical knowledge
• Frequent lagging issues

Improving Annotation Quality and Machine Learning Output with HITL Approach

A human-in-the-loop approach uses human intelligence to verify and correct machine-generated annotations. This approach can significantly enhance the quality of annotations in several ways:

1. Error Correction & Reduction: Due to AI and Machine-learning algorithms’ limitations, the generated annotated results can be error-prone. The involvement of subject matter experts can help find and correct those errors for improved accuracy.
2. Better Accuracy & Reliability: For efficient working, machine learning models require a large number of annotated data points. If a rare dataset has limited information online, a machine learning model can only annotate it correctly if a subject matter expert provides the necessary domain details.
3. Reduce Ambiguity: Some information can be unclear to machine learning models, leading to incorrect labeling. Humans can provide context and disambiguate the annotations, leading to better-quality labels.
4. Domain-Specific Knowledge: Subject matter experts have domain-specific knowledge that can improve the annotation quality. For example, a human may recognize a particular image as a rare bird species that a machine may not have encountered before.

How to Implement HITL Approach in Your Organization?

Here are several ways to implement the human-in-the-loop approach in your project/business to get efficient outcomes:

1. Choose the right tools

Choose those annotation tools that support HITL verification/approach (like the ones mentioned in this guide) to verify the accuracy of annotated data.

2. Set up the workflow with clear guidelines

Develop an efficient workflow that involves the HITL approach for the final verification of annotated outputs. For example, for initial labeling, use AI annotation tools and then verify the details with subject matter experts for high data accuracy.

3. Hire experienced annotators or outsource

For efficient and high-quality annotation, hire professional annotation experts who have domain-specific knowledge to perform the task. You can outsource your annotation projects to a trusted data annotation company to focus on core business operations and save resources.

4. Monitor the process and measure the impact

Monitor the annotation quality in terms of accuracy and consistency after using the HITL approach to evaluate its impact.

Conclusion

These are some reliable data annotation tools you can consider for your project needs depending on the type of data you wish to annotate. Some of these tools also support human-in-the-loop verification to improve the quality and accuracy of automated annotation results. However, as a growing business, you can also consider outsourcing annotation services to experts in order to improve operational efficiency, save time, and conserve resources.

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