OCR Accuracy Testing
OCR (optical character recognition) accuracy testing measures how correctly a system extracts text from images or scanned documents, tracked via character error rate and word error rate against a hand-verified ground-truth transcript, across the fonts, layouts, handwriting, and image quality the system will actually encounter in production.
OCR accuracy varies enormously by input type, so a single "accuracy" number is close to meaningless without specifying what was tested — clean printed text, low-resolution phone photos, handwriting, dense tables, and rotated or skewed scans all have wildly different error rates from the same engine. A test suite that only uses clean sample documents will show near-perfect accuracy and then fail badly the moment real users start uploading crumpled receipts photographed at an angle.
The standard metrics are character error rate (CER) and word error rate (WER) — edit distance between the extracted text and a verified ground truth, normalized by length — tracked separately per document category rather than averaged into one number that hides which categories are actually failing. Table and form extraction (where structure matters as much as the text itself) usually needs its own test category, since an OCR engine can transcribe every character correctly while completely losing which cell each value belongs to.
