There is no universal quality score. OCR.now reports the recognized word count and engine confidence so you can compare controlled source changes without pretending the number certifies accuracy.
The page combines recognized words, page count, engine confidence, and visible inspection so improvements are judged by real error reduction rather than one attractive number.
Use How to Improve OCR Accuracy: A Controlled Checklist to change the source deliberately and How to Measure OCR Accuracy with CER and WER to measure errors. For a document-level control, compare OCR Document Compare; for a normal extraction, return to Image to Text.
Diagnose what the confidence number cannot tell you
| Review point | Likely failure | Validation action |
|---|---|---|
| Controlled test | Different passages are compared | Run the same difficult crop after each source change |
| Error impact | All character errors receive equal weight | Separate cosmetic errors from consequential field errors |
| Confidence | A high average is treated as proof | Sample high-confidence text and inspect mandatory fields |
Change one variable and measure the same sample
Test the same representative passage after each source change.
Record exact corrections, character size, and confidence together.
Choose the source with fewer consequential errors, not the prettiest filter.
Source-quality experiment
To compare a raw scan with a deskewed copy, recognize the same paragraph containing small type, punctuation, a name, and a number. Record exact corrections and confidence for both. Prefer the version with fewer consequential errors, even if its visual contrast looks less dramatic.
When field-level ground truth is required
Use character error rate or word error rate against independent ground truth when measuring a collection formally. Use sample-based quality assurance when full manual review is not practical.