What is Data Annotation: Need, Types, and Tools

Data Annotation is the process of labelling and annotating different kinds of data and creating an annotated data set that can be used in the development and training of multiple AI models. Such data becomes useful for AI model training as assigning and labelling raw data provides context and structure to the information, making it understandable and accessible to machines.

In this blog, let's dive deep into the world of data annotation and labelling and learn about some tools that are used in the industry.

_{The Need for Data Annotation}

Unlabeled and unstructured raw data is impossible for computer systems such as AI to understand and interpret, making it extremely difficult to extract meaningful insights. 

For instance, an AI system could not point out the difference between a dog and a cow in an image unless labelled differences in the form of annotated data were fed into it, from which it could take reference and learn the difference.

Since the ultimate goal is the development and training of such intelligent systems, data annotation becomes the most critical job for the future of intelligent systems. It bridges this gap by adding annotations, metadata, or labels to raw data, transforming it into a valuable resource for training machine learning models.

_{Enhancing Data Quality and Reliability}

Data annotation improves the quality and reliability of data by adding additional information to it. With the labels and tags added, it becomes easier for humans as well as computer systems to understand and better categorize them.

For example, labelled and annotated data in the form of videos, images and texts are used as a reference by AI systems in self-driving cars to identify roads, pedestrians, road signs, other cars, etc.

_{Enabling Machine Learning and AI Applications}

Data annotation is the primary step in the training of Machine Learning and Artificial intelligence (AI) models. Through annotation, datasets can be enriched with labelled examples that enable machines to learn patterns and make accurate predictions.

_{Types of Data Annotation}

Data annotation is of multiple types, depending on the kind of data being annotated. Majorly, there are three types of data annotations:

_{Image Annotation}

Image annotation refers to labelling objects or regions of interest within an image. This provides more context to raw images and makes it information heavy. Some of the most common image annotation techniques are:

• Bounding Box Annotation:
  Wherein the annotator is required to draw rectangular boxes around objects of interest in the image.

• Semantic Segmentation Annotation:
  It involves labelling individual pixels in an image to show the boundaries of different regions in an image. This kind of annotation enables machines to understand the edges of different objects.
  

• Polygon Annotation:
  In this type of annotation, annotators plot the exact edge points of objects in an image, regardless of the shape of the object. It allows high-level edge detection.

• Image Classification:
  Image classification is about understanding an image by giving it a label. It involves identifying and categorizing the image's overall class instead of a specific object. Typically, it applies to images with a single object.

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_{Text Annotation}

Text annotation mainly involves labelling and categorising textual data. Some popular text annotation techniques include:

• Named Entity Recognition:
  NER means identifying and classifying named entities, such as names, organizations, locations, or dates, within the text. It also includes Keyphrase tagging and Part-of-speech(POS) tagging.
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• Text Classification Annotation:
  Intent classification annotation is the process of labelling text data with the corresponding intent or purpose. Mostly used in customer service chatbots, such data understand the query or request of the customer to direct them to the appropriate resolution.

• Sentiment Analysis Annotation:
  This technique aims to determine the sentiment or emotional tone expressed in a text. An annotator is required to select from a set of tones that the text can be attributed to.
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_{Video Annotation}

Video annotation deals with labelling objects, actions, or events within a video. It has a wide area of applications such as surveillance, autonomous driving, video editing, etc. Some of the most common video annotation techniques include:

• Action Recognition Annotation: 
  It focuses on identifying and labelling specific actions or movements performed by objects or individuals in a video.
  

• Object Tracking Annotation: 
  It means tracking and labelling the trajectory of objects of interest as they move across frames.
  

• Event Annotation: 
  It involves annotating events or activities captured in a video and providing contextual information.

_{Annotation Tools}

Now, the process of annotation might seem easy and straightforward, but the amount of data being annotated to make a significant difference is huge. To help annotators and AI trainers make the process smoother, several annotation tools have been developed. Here are some widely used tools for data annotation jobs:

1. Labelbox: Labelbox offers a comprehensive platform for simplified data annotation, providing features like image segmentation, object tracking, and sentiment analysis annotation.

2. RectLabel: Primarily designed for image annotation, RectLabel offers an intuitive interface and supports bounding box annotation, image classification, and landmark annotation.

3. VGG Image Annotator (VIA): VIA is an open-source annotation tool that supports various annotation types, including image segmentation, object detection, and keypoint annotation.

4. BRAT: BRAT is a text annotation tool specifically designed for annotating textual data. It supports entity recognition, relation annotation, and event annotation.

5. Prodigy: Prodigy is a powerful annotation tool with a focus on active learning. It provides an interactive environment for annotators and integrates seamlessly with machine learning workflows.

_{In a Nutshell}

Data annotation is a vital process that enables machines to understand and learn from raw data. By assigning labels and tags to unstructured data, annotation provides context and structure, making it easier for machines to extract insights. With various annotation techniques and tools available, data annotation has become an integral part of modern machine learning and AI applications. 

Lastly, Data annotation is the primary job of an AI Training Specialist. These trainers are the force behind the development of the trending Large Language models (LLMs) like GPT, AI Image Generators like DALL-E and MidJourney, and all the other AI-assisted tools and software. 

If you are interested in building a career in the AI industry, learning Data Annotation and Labelling and how to become a trained AI Training Specialist amidst the career challenges, join FlexiBench today and unlock exciting new career opportunities!

FAQs

What do you mean by annotating data?

Annotating data refers to the tagging, labelling, and classification of raw data to convert it into annotated data sets that can be understood by machines and can be used for training and development of AI models.

What is the salary of a data annotator?

The range of salaries for a data annotator ranges from 2 LPA to 8-9 LPA for specialised and experienced data annotators. The upper ceiling is not fixed as the field is still growing.

Is data annotation a good job?

Yes, Data annotation is one of the fastest-growing tech job sectors with great prospects for the future.