What is OCR?
Optical character recognition converts text visible in images or scanned documents into machine-readable characters. Output may include plain text, positions, confidence values, or structured fields.
How OCR works
An OCR pipeline first prepares a raster, locates text regions, segments or recognizes lines and words, and maps visual patterns to characters. Layout analysis can then reconstruct reading order, tables, blocks, and coordinates rather than returning one undifferentiated string. Language models and dictionaries help disambiguate glyphs, but they can also normalize away unusual names or codes. In media management, OCR creates searchable metadata and automation inputs while the source image remains the evidentiary artifact.
Key facts
- Deskewing, orientation detection, contrast normalization, and adequate sampling can improve recognition before the language model ever evaluates a character.
- Character confidence is not a calibrated guarantee of field correctness; totals, identifiers, and names often need domain validation or human review.
- Searchable PDFs commonly pair a page image with an invisible text layer, so visual fidelity and selectable text can disagree when recognition is wrong.
When OCR matters
Use OCR to make scans searchable, automate data entry, or supply extracted text to indexing and AI workflows. Low resolution, unusual layouts, handwriting, or poor contrast can reduce accuracy and require review.