Document AI
OCR Evaluation for Documents, Forms, and Tables
Measure OCR text accuracy and downstream field extraction across scans, layouts, languages, and confidence thresholds.
An OCR system can transcribe most words correctly and still miss the invoice total or scramble a table. Test both the text and the information your workflow needs.
Build a representative document set
Sample scans and native PDFs from each document family. Include faint text, skewed pages, stamps, handwriting if relevant, multiple columns, tables, and the languages used in production. Label a held-out set with both transcription and key fields; record document version and annotation rules. Google Document AI distinguishes digitizing text from extracting forms and layout, a useful distinction when defining the task.
Use separate error measures
Measure character or word error after a stated normalization policy. Then measure field precision and recall for dates, totals, names, and checkboxes. For tables, check row and column associations; a correct number in the wrong cell is still an error. The Document AI evaluation guide describes comparing predicted entities with annotated test documents and how a confidence threshold changes precision and recall.
Inspect failures and cost
Review the highest-impact false positives and false negatives by document type. Count pages that fail entirely, not only successful pages. Record processing time, price per page, privacy requirements, and whether human correction is needed. If one framework needs preprocessing, include that step in the measured pipeline for every candidate.