OCR Accuracy Calculator
A vendor claim of 98 percent OCR accuracy can coexist with only 70 percent of invoices passing without a touch. The difference is field level versus document level accuracy, and this calculator shows both from one reviewed sample, along with the correction hours they imply.
Your numbers
Count only the fields you actually need downstream.
Your results
Accuracy from a small sample carries uncertainty. Use a few hundred documents and a random draw before quoting the figures externally.
Benchmark your documents against a private OCR pipeline
Share an anonymised sample and we will run it through an on-prem extraction setup and report accuracy per field. Optional follow-up.
Optional. The tool and every export work without this. No spam.
How to use the calculator
Draw a random sample of documents, run them through your capture tool, and have a person compare each extracted field with the source. Count the fields checked per document and the fields that were wrong or missing. Also count how many documents contained at least one error. Enter those numbers with your correction time per bad document and monthly volume. The calculator returns field accuracy, document accuracy, errors per thousand fields and monthly correction hours.
- Sample at random, not only the clean documents
- Count a missing field as an error
- Check only fields used downstream
- Keep the same field list when you re-measure
The formulas
Field accuracy equals one minus errors divided by total fields checked. Document accuracy equals one minus documents with errors divided by documents sampled. If errors were independent across fields, document accuracy would be roughly field accuracy raised to the number of fields, which is why 98 percent per field over 14 fields gives only about 75 percent of clean documents. Real errors cluster on poor scans and unusual layouts, so measured document accuracy is often better than that theoretical figure.
Reading the result
Use document accuracy to forecast touchless rate and staffing, and field accuracy to decide where tuning pays off. Break results down by vendor, document type and scan quality before acting, because a handful of suppliers usually cause most errors. Compare the correction hours with the cost of the tool: if hours stay high after tuning, the issue is often layout variety rather than model quality, and a template or supplier change request can be cheaper.
- Under about 90 percent document accuracy, plan for large review capacity
- Look at errors by field to find the one or two worth fixing
- Re-measure after any model, template or scanner change
Frequently Asked Questions
What is the difference between field and document accuracy?
Field accuracy counts each extracted value separately. Document accuracy counts a document as correct only if every field is right. Document accuracy is always lower and is the better predictor of how many documents can post without human review.
How big should the sample be?
At least 200 documents is a reasonable minimum for a headline figure, more if you want per vendor results. Fewer documents leave wide uncertainty, so treat small samples as indicative only.
Is confidence score the same as accuracy?
No. Confidence is the tool's own estimate for a value. Accuracy is what you measure against the source. A well calibrated tool has confidence close to actual accuracy, but you must verify that on your own data.
Does a character error rate matter?
Character error rate is useful for comparing raw OCR engines, but business outcomes depend on whole field correctness. A single wrong digit in an invoice number or amount makes the field wrong, so field level measurement is the better business metric.
If you want accuracy measured continuously from your real documents and ERP outcomes instead of one-off samples, we can wire up a private extraction pipeline and a review queue on your own infrastructure.
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