Algorithms have become an integral part of our daily lives, no matter if you are shopping for anything on Amazon or steaming a web series on Netflix or Hotstar, algorithms make seemingly complicated functions simpler. We must tell computers what they are going to interpret and give them a context to make decisions because they cannot process visual information in the same way as the human brain. This ability of algorithms to deliver on these promises depends on data annotation – the act of accurately categorizing information to educate artificial intelligence to draw conclusions. In short, data annotation drives our algorithm-driven world.
What is Data Annotation?
Data annotation is the human activity of tagging content such as text, photos, and videos so that machine learning models can recognize them and use them to generate predictions.
When we label elements in the data, ML models accurately understand what they are going to process and maintain that information to automatically process the available information, built on existing knowledge to make decisions.
Types of Data Annotations
Each data form has its own labeling procedure, so here are some examples of the most common types:
Image annotation ensures that machines perceive an annotated area as a different item. When such models are trained, captions, identifiers, and keywords are added to them as attributes to images. The algorithms then identify and understand these parameters and learn autonomously. It usually involves the use of bounding boxes and semantic segmentation to be used in a range of AI-based applications like facial recognition, computer vision, robotic vision, autonomous vehicles, among others.
Video annotation, like image annotation, uses techniques such as bounding boxes to recognize motion frame-by-frame or using a video annotation tool. The data obtained from video annotation is essential for computer vision models that perform object location and tracking. Video annotation allows seamless implementation of concepts like location, motion blur, and object tracking, in the systems.
Text annotation is the process of assigning categories to sentences or paragraphs in a given document based on the topic. This text can be anything, starting from consumer feedback to product reviews on shopping sites, from a mention on social media to email messages. Since texts convey intentions in the most straightforward way, there is a lot of scopes to derive useful information from them using text annotation. The process of text annotation is a bit tricky and has a lot of stages because machines are unfamiliar with concepts and emotions like fun, sarcasm, anger, and other abstract elements.
Audio data comprises more dynamics like language, speaker demographics, dialects, mood, intention, emotion, and behavior. Audio annotation requires identification of such parameters followed by tagging using techniques such as timestamping, music tagging, and acoustic scene classification, among others. Besides verbal cues, nonverbal instances such as silence, breaths, and even background noise can also be annotated for a comprehensive understanding of the available audio file.
Semantic annotation involves tagging concepts like people, places, or company names within a document to help ML models categorize new concepts in the future text. It is a critical component of AI training to improve chatbots and search relevance. Semantic annotation mainly involves tagging of key phrases and the appropriate identification parameters; it has a crucial role to play in-text annotation.
Data Annotation Tools
Some of the great open-source tools that will help you automate the tagging process are –
- Amazon SageMaker Ground Truth
- Ground Truth Labeler – MATLAB & Simulink
- Computer Vision Annotation Tool (CVAT) by Intel
- Visual Object Tagging Tool (VoTT) by Microfost
- Scalabel – A web-based visual data annotation tool
Future of Data Annotation
According to Visual Capitalist, an estimated 464 exabytes of data will be created daily around the world in 2026. In addition, according to Global Market Insights, the global market for data annotation tools is expected to grow approximately 40% annually over the next six to seven years, especially in the automotive, retail, and healthcare sectors. Considering the current pace of data generation, data annotation is a crucial and impressive endeavor. It will maintain its usefulness across AI and machine learning-based applications
With data annotation, an AI model would know if the data it receives were audio, video, text, graphics, or a combination of formats. Based on the functionalities and assigned parameters, the model classifies the data and gives it the green signal to perform its tasks. Your models are properly trained only after you implement data annotation and you get optimal results and a foolproof model for any task, such as chatbots, image recognition speech recognition, automation, etc.
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