# OCR Field Accuracy to Document Accuracy Calculator > Source: https://netray.co/tools/ocr-field-accuracy-to-document-accuracy-calculator > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer See why 98 percent field accuracy does not mean 98 percent clean documents, and what field accuracy a target requires. ## Category quality ## Industry Manufacturing ## Audience - Quality engineer - AP manager - Document control lead ## Tool **Kind:** calculator **Inputs:** #### Fields checked per document **Key:** fields_per_doc **Type:** slider **Default:** 15 **Min:** 1 **Max:** 60 **Step:** 1 **Unit:** fields **Help:** Count only fields that must be right to post or accept the document. #### Field-level accuracy **Key:** field_accuracy **Type:** number **Default:** 98.5 **Min:** 0 **Max:** 100 **Step:** 0.1 **Unit:** % #### Documents per month **Key:** docs_per_month **Type:** number **Default:** 4000 **Min:** 0 **Step:** 100 **Unit:** docs #### Target error-free documents **Key:** target_doc **Type:** number **Default:** 95 **Min:** 1 **Max:** 100 **Step:** 1 **Unit:** % **Outputs:** #### Expected error-free documents **Key:** doc_accuracy **Expr:** pow(field_accuracy / 100, fields_per_doc) * 100 **Format:** percent **Primary:** true **Description:** Assumes errors on fields are independent. #### Expected wrong fields per document **Key:** errors_per_doc **Expr:** fields_per_doc * (100 - field_accuracy) / 100 **Format:** decimal #### Documents with at least one error per month **Key:** docs_with_error **Expr:** docs_per_month * (100 - doc_accuracy) / 100 **Format:** integer #### Field accuracy needed for the target **Key:** required_field **Expr:** pow(target_doc / 100, 1 / fields_per_doc) * 100 **Format:** decimal **Primary:** true **Description:** Per-field accuracy that yields the target share of clean documents. **Chart:** #### Error-free documents vs fields per document **Type:** line **X:** fields_per_doc **From:** 1 **To:** 60 **Steps:** 30 **Series:** - doc_accuracy **Disclaimer:** Assumes independent errors. Real errors cluster on poor scans, so the true document accuracy is often somewhat higher than this model for the same field accuracy. ## Content **Intro:** A vendor says 98.5 percent accuracy. Your reviewers say a third of the invoices still need fixing. Both can be true, and this calculator shows why: accuracy compounds across the fields on a document. **Sections:** #### How to use the calculator **Body:** Enter how many fields on each document must be correct, the measured field-level accuracy and monthly volume. The first result is the expected share of documents with no errors. Enter a target to see the per-field accuracy required to reach it. Use the chart to see how adding fields, such as line items, drives document accuracy down. **Bullets:** - Count header fields and each line item field that you validate. - Use accuracy measured on your own documents, not a benchmark. - Try a version with only critical fields to see the effect of narrowing scope. #### The math **Body:** If every field is right with probability p and errors are independent, a document with n fields is clean with probability p to the power n. The accuracy needed for a document-level target T is T to the power 1/n. At 98.5 percent and 15 fields the clean rate is around 80 percent, so one document in five has an error. At 99.5 percent it climbs to about 93 percent. This is why small field-accuracy gains matter more than they appear. #### How to read the result **Body:** Compare the clean-document rate with your review rate. If the model says 80 percent clean but only 60 percent of documents pass without correction, errors are clustering on bad scans or specific suppliers, and fixing those sources will help more than a generic model change. Remember that not every error matters equally: a wrong remit-to address is worse than a wrong description. #### Common mistakes **Body:** Measuring accuracy at character level and quoting it as field level overstates quality. Averaging accuracy across easy and hard suppliers hides the tail. And treating the model as exact for line items is risky because errors there are far from independent: a misread table structure breaks all rows at once. **Cta:** To measure your real field and document accuracy on a sample of your own documents, use the audit worksheet in this batch, or ask Netray to review the results. **Faq:** #### Why is document accuracy lower than field accuracy? **Answer:** A document is clean only if all of its checked fields are right. Even high field accuracy compounds across many fields, so the share of fully clean documents is lower than the per-field figure. #### Are OCR errors really independent? **Answer:** Not entirely. Poor scans and unusual layouts cause several errors at once, so real clean rates are often a bit higher than the independent model predicts. Treat the model as a conservative planning figure. #### What is the difference between character and field accuracy? **Answer:** Character accuracy counts each character read correctly. Field accuracy counts a whole value as correct only if every character is right, so it is always lower than character accuracy. #### Which fields should I count? **Answer:** Count fields that must be correct to post or accept the document, such as vendor, invoice number, date, totals, PO number and part numbers. Optional or informational fields can be excluded. ## Search terms OCR field accuracy vs document accuracy, OCR accuracy calculator, document level accuracy, error free document rate, OCR accuracy compounding, required field accuracy for target, extraction accuracy per document --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json