What Is Annotation (AI)?
Annotation in AI refers to the process of adding structured metadata, labels, or markings to raw data — including bounding boxes on images, entity tags in text, or timestamps in audio — to create the labeled datasets required for training machine learning models.
How Annotation (AI) Works
Annotation is the practical execution of data labeling, encompassing the specific methods and tools used to mark up different types of data. Image annotation includes bounding boxes, polygons, semantic segmentation masks, and keypoints. Text annotation includes named entity recognition, part-of-speech tagging, sentiment labels, and relationship extraction. Audio annotation includes transcription, speaker diarization, and emotion labeling. The annotation process requires clear guidelines, quality control mechanisms, and often domain expertise. Modern annotation platforms offer AI-assisted annotation where the model pre-labels data and humans correct mistakes, significantly speeding up the process.
Real-World Examples
An annotator drawing precise polygon outlines around each organ in medical CT scan slices for a segmentation model
A team annotating thousands of customer emails with intent labels like 'refund request,' 'shipping inquiry,' and 'product question'
Linguists annotating text with named entity tags to mark people, organizations, locations, and dates for NER training