Data annotation is the process of adding meaningful and informative labels or tags to raw data such as images, videos, text, or audio to make it understandable and usable by machine learning (ML) and artificial intelligence (AI) models. This labeling provides context and categorization that helps algorithms recognize patterns, objects, or information within the data, enabling them to learn and make accurate predictions or decisions
. The process involves identifying and labeling specific features in the data, such as objects in images, sentiment in text, or speech in audio. This can be done manually by humans or with the aid of automated tools, often followed by quality checks to ensure accuracy
. Data annotation is especially critical for unstructured data, which constitutes a large portion of the data generated today, including emails, social media posts, sensor data, and multimedia content
. Data annotation is foundational for training AI models because it establishes the "ground truth" - the correct answers or labels that the model learns from. High-quality annotated data improves the accuracy, reliability, and performance of AI systems across various applications like computer vision, natural language processing, autonomous vehicles, and medical diagnostics
. In summary, data annotation bridges the gap between raw data and machine understanding by systematically labeling data to train AI models effectively
