Data Annotation

Data annotation is the process of labeling or tagging raw data – such as text, images, audio or video – with relevant information to make it understandable for machine learning models. This annotated data serves as the foundation for training supervised artificial intelligence systems, helping them recognize patterns, make predictions, or classify inputs accurately. Common types of annotation include bounding boxes for object detection, sentiment labels for text and transcriptions for speech. High-quality data annotation is critical for model accuracy, and it is widely used in applications like computer vision, natural language processing, autonomous vehicles and medical diagnostics.