An OCR audit is the process of checking text that's been automatically extracted from images or scanned documents against the original source, to catch errors the OCR software introduced, misread characters, missing punctuation, or formatting that got jumbled during extraction.

‍Why it matters

OCR technology has gotten a lot better, but it still reliably makes mistakes on things like handwriting, low quality scans, unusual fonts, or documents with tables and multi-column layouts. Those errors matter a lot depending on what the extracted text gets used for, a misread number in a financial document or a garbled clause in a legal contract isn't just a cosmetic typo, it can change the actual meaning of the document. An OCR audit exists to catch that before the extracted text gets treated as reliable.

‍How it's done

Auditors typically compare the extracted text side by side against the original scanned image, checking a sample rather than every single page for very large documents, and flagging specific error patterns like commonly confused characters or a font that engines like Tesseract or Google Cloud Vision consistently struggle with. Those patterns often get fed back into retraining the OCR model or adjusting preprocessing steps, like image cleanup before text extraction even happens, rather than just correcting the same mistakes by hand every time.