OCR annotation is the labeling of text that appears within images or documents, marking where each piece of text is, usually with boxes or polygons, and transcribing what it says. It is the training data behind optical character recognition and document AI.
OCR (optical character recognition) annotation sits at the intersection of vision and text: the data is an image, but the labels capture the words inside it. Annotating it has two parts, localization, drawing a box or polygon around each text region, a line, a word, sometimes a character, and transcription, typing exactly what that region says, including punctuation and case.
Richer OCR annotation also captures reading order, how lines flow, structure, headings, tables, key-value pairs in a form, and language. It is what trains OCR engines and the document-AI models that read receipts, forms, IDs, and scanned pages.
Key takeaways
OCR annotation labels text in images, both where it is, boxes or polygons, and what it says, transcription.
It bridges visual and text annotation: visual localization plus textual content.
Document AI extends it with reading order, structure, and key-value relationships.
What OCR annotation provides
What OCR annotation captures.
What OCR annotation captures.
Element
What it captures
Text localization
Boxes or polygons around lines, words, or characters
Transcription
The exact characters, with case and punctuation
Reading order
The sequence in which text should be read
Structure
Headings, tables, and form fields, for document AI
How it works
Annotators, often aided by an existing OCR model, box the text regions and verify or correct the transcription, then capture order and structure if needed. FiftyOne can store and visualize the image with its text-region detections and labels, so you can review localization quality and curate the documents worth annotating, the visual side of the task.
Why it matters
OCR annotation powers a huge share of practical document automation, anywhere a machine reads text off a photo or scan. The two halves fail differently and must be reviewed separately. Localization errors, a box that clips a character, and transcription errors, a misread "0" versus "O", are independent, and a label can be perfectly boxed yet wrongly transcribed, so checking only the boxes, the visible part, misses half the error. The hardest, most valuable data is the messy real-world stuff, skewed scans, handwriting, low contrast, where both localization and transcription degrade together, while clean printed text adds little.
Frequently asked questions
What is OCR annotation?
Labeling text in images, marking where it is and transcribing what it says, to train OCR and document AI.
What are the parts of OCR annotation?
Localization, boxing the text, and transcription, the exact characters, plus reading order and structure for documents.
How is OCR annotation different from text annotation?
Text annotation labels existing text for NLP. OCR annotation first finds and transcribes text inside an image.