Optical Character Recognition (OCR)

OCR first needs a suitable image representation and an interpretation of where text appears. Recognition maps visual patterns to characters or sequences, while layout and reading-order processing determine how those sequences are assembled. Language resources and contextual modeling can improve recognition but may also normalize an unusual identifier into a plausible wrong word. OCR differs from parsing a born-digital file that already contains text, and from understanding a document's meaning. A high-quality transcription can still lose table relationships or pair a value with the wrong label, so character accuracy is only one component of document extraction quality.

The practitioner samples real pages, chooses engines and language settings and evaluates preprocessing such as rotation correction or contrast adjustment. They preserve page provenance and, where available, positions or confidence information. Useful artifacts include a test set with reference transcriptions and error analysis for critical fields. Evaluation should measure downstream consequences as well as character errors, particularly for identifiers, dates and amounts. The pipeline routes uncertain or unsupported pages for review and distinguishes an empty page from a recognition failure rather than silently accepting all output as valid text.

Handwriting, poor scans, unfamiliar languages and complex layouts can substantially reduce accuracy. Confidence scores are engine-dependent and do not guarantee correctness. Preprocessing can improve one page type while damaging another, and language correction can alter codes or names. OCR quality should be evaluated on representative material and critical-field consequences, not a clean demonstration page. Recognition also does not validate the truth or authenticity of the document it transcribes.