Top Data Labeling Tools 2019
trantorindia | Updated: March 14, 2019
Why are machines so smart?
Because we make them so.
But they can be only as smart as we make them to be.
In today’s AI ecosystem, there is pressure on everyone to make their machine learning algorithms as good as human intelligence. And the only way to achieve it is to have a good amount of quality labeled data to train those algorithms.
Except that the data doesn’t come easy.
Every organization entering into machine learning related services faces this challenge today. And to overcome it, they must have the know-how of different data labeling tools that can help in building quality training data sets and build them efficiently.
Here is a list of the best data labeling tools based on what type of data you are labeling.
Image & Video Labeling Tools
It’s a free, easy to use, MIT-licensed annotation tool for labeling of images on a website. It’s free for commercial use as well. Integrating it with your website only requires adding 2-3 lines of code. You can also explore many of its features in the demos.
Annotorious’ most noticeable features include:
- Image annotation with bounding boxes
- Process maps and high-resolution zoomable images
- Annotorious can be modified with plugins to suit a particular project’s need
- Annotorious Community; where developers can find how they can modify it to extend its capabilities
- Annotorious Selector Pack plugin (to be launched), which will include features like custom shape labels, freehand, point, and Fancy box selection
LabelMe is an open source online data labeling tool. With simple signup, it allows users to label images and share their annotation publically, which is primarily used for a range of computer vision based applications and research.
Some of LabelMe’s key features include:
- LabelMe also offers its mobile app for image labeling & annotation
- Image collection, storage, and labeling
- Training object detectors in real-time
- Simple and intuitive UI
- LabelMe offers MATLAB Toolbox that allows users to download and interact with the images and annotations in the LabelMe database
Labelbox is one of the most versatile labeling tools available today. Its comprehensive features enable organizations to easily adapt and train their machine learning models. Its pricing varies based on the amount of data and the sophistication of the model you are training.
Key features include:
- Labelbox supports Polygon, Rectangle, Line, and Point segmentation, as well as pixel-wise annotation
- You can create bounding boxes and polygons directly on the tiled imagery (zoomable maps)
- Ideal to work with a big team of labelers as it serves up images to be labeled asynchronously, i.e., no two labelers label the same image
- Assured security as the source data is either stored in-house or on a private cloud
- Labelbox allows you to maintain quality standards by keeping track of labeling task performance
Sloth is a versatile annotation tool for various data labeling tasks related to computer vision research. It’s free and is one of the most popular tools for facial recognition, therefore, is widely used for surveillance and user identification related applications.
Most notable of Sloth’s features include:
- It allows an unlimited number of labels per image or video frame – leading to more detailed file processing
- It supports various image selection tools – points, rectangles, and polygons
- Developers consider Sloth as a framework and set of standard components that can be configured to build a label tool specifically tailored to one’s needs
Audio Labeling Tools
Praat is a free audio labeling tool under the Creative Commons (CC BY SA) license, meaning, any derivative works must also come under creative commons license.
Praat’s primary features include:
- Spectral analysis, pitch analysis, format analysis, and intensity analysis of audio files
- It can also identify jitter, shimmer, voice breaks, cochleagram, and excitation pattern
- You can work with sound files of up to 3 hours (2GB)
- It allows you to mark time points in the audio file and annotate these events with text labels in a lightweight and portable TextGrid file
- Users can work with sound and text files at the same time when text annotations are linked with an audio file
Aubio is another free and open source annotation tool for audio data labeling. The tool is designed to extract annotations from audio signals. Aubio is written in C and is known to run on most modern architectures and platforms.
Aubio offers the following key features:
- Digital filters, phase vocoder, onset detection, pitch tracking, beat and tempo tracking, mel frequency cepstrum coefficients (MFCC), transient / steady-state separation
- You can segment a sound file before each of its attacks, performing pitch detection, tapping the beat and producing midi streams from live audio
- There’s a dedicated function library to execute above-mentioned functions in real-time applications
- Users can also use these functions offline via sound editors or software samplers
Speechalyzar is an audio data labeling tool specifically designed for the daily work of a ‘speech worker’. It can process large speech data sets with respect to transcription, labeling, and annotation. Its main application is the processing of training data for speech recognition and classification models.
Speechalyzar’s main features include:
- You can implement it as a client-server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation
- Speechalyzar also allows you to perform benchmarking tests on speech-to-text, text-to-speech and speech classification software systems
- Ideal for manual processing of large speech datasets
EchoML by Azure and Soundscape are some other audio data labeling tools with rich visualization capabilities that you can also explore.
Text Labeling Tools
Rasa NLU is an open-source NLP tool for intent classification and entity extraction. It is primarily used to annotate text for chatbots but can be used for a variety of applications. For instance, recently Trantor used Rasa NLU to train a machine learning model to detect harassment and abuse in email communication within an organization.
Some of the advantages with Rasa NLU are:
- Users can tag multiple words in a single sentence to their respected class or assign the same word in multiple classes
- You can customize and train its language model as per domain-specific needs and get higher accuracy
- Rasa NLU’s open source library runs on premise to keep users’ data safe and secure
Stanford CoreNLP is a free, integrated NLP toolkit that provides a set of human language technology tools, which allow users to accomplish various text data pre-processing and analysis tasks.
Here are some advantages with Stanford CoreNLP:
- It offers a broad range of grammatical analysis tools (base forms of words, parts of speech, names, normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies)
- CoreNLP is a fast, robust annotator for arbitrary texts, widely used in production
- It offers a modern, regularly updated package, with the overall highest quality text analytics
- Support for a number of major (human) languages
- It offers APIs for most major modern programming languages and can run as a simple web service
Bella is a text annotation tool that helps data scientists manage, label, and evaluate natural language datasets. It is designed to save time spent in measuring and learning data, which involves collecting, inspecting, training, and testing data.Image: A Bella project file for labeling a social media post (Source: Github)
Some plus points of using Bella:
- It offers an intuitive GUI, which allows users to label and tag data through convenient keyboard shortcuts and swipe, and visualize metrics, confusion matrices, and more
- Bella also offers database backend to easily manage labeled data
- Bella is a preferred tool for sentiment analysis, text categorization, entity linking and POS tagging
Tagtog is a versatile text labeling tool that offers manual as well as automated annotation. It’s an AI startup with an impressive client base including AWS, Siemens, and a number of data science research institutions.
Some of the best things about Tagtog are:
- Users don’t require coding knowledge or data engineering concepts to use Tagtog
- Tagtog offers inbuilt ML model to automate text annotation and also provides hassle free deployment and maintenance of manually trained models
- Tagtog annotation tool allows multiple users to collaborate to a single project
There are numerous other data labeling tools in the market, apart from the ones listed above. And as with any other tool for any other purpose, the key is not to know a lot of tools but to know which tool will work best for a given project and to understand how to leverage it best.
And as for which approach you should adopt for labeling – in-house or outsource – that also depends on the project requirements. If you have time and resources, you can do it in-house. If priority is to cater customers with AI driven solutions as quickly as possible, then it is suggested to outsource your projects to a professional firm.
The machine learning market is brewing up and companies are in a rush to get ahead of each other. So, in current dynamics, spending a little to take the advantage of the early bird can make a big difference in the long run.