01
The process begins before OCR
Documents may arrive by post, email, form, EDI or portal. First identify the type, sender and case, then verify that the file is readable and complete.
A controlled intake point, case identifier and original-file policy reduce the disorder automation would otherwise inherit.
02
Extraction is a hypothesis
OCR or AI returns a value with uncertainty. Invoice numbers, amounts, dates, tax identifiers and line items need format, total and counterparty checks.
Low-confidence or conflicting fields should go to a person. The correction view should show the relevant document area and retain an audit trail.
03
Connect data to a decision
Validated data should trigger approval, duplicate checks, matching and a controlled write to the financial or operational system. Each step needs a clear success confirmation.
- unique document identifier;
- original file and change history;
- approval roles and value thresholds;
- error and retry log;
- retention appropriate to the document.
04
Measure end-to-end quality
Average OCR accuracy is not enough. Track critical-field accuracy, touchless processing, correction time, duplicates and errors found after posting.
Test varied layouts, weak scans, corrections and multi-page documents so the pilot is not based on perfect examples.
SOURCES
Verified source material
Information was checked on the publication date. Legal obligations should always be assessed for the specific organisation and use case.