An invoice-recognition system may capture addresses and descriptions accurately but misread an account identifier. A high overall score would not make the resulting transaction safe to process automatically.
Identify consequential fields
List the values that affect matching, routing or approval. Define how each is checked and what happens when the value is uncertain.
Amazon Textract’s best-practice guidance discusses confidence scores and input quality. Its supported formats and languages must be checked for the actual deployment; a recognisable product name does not imply universal document coverage.
An illustrative APAC workflow may contain mixed-language invoices and scanned attachments. Test the actual combination rather than infer performance from clean English samples.
Compare extracted values with authorised annotations
Keep a test set with verified field values and a clear matching rule. Decide whether differences in punctuation or formatting matter to the downstream task.
Record human correction effort, not only extraction accuracy. A field that is easy to identify as uncertain may be less costly than a plausible but wrong value that bypasses review.
Avoid using the same corrected documents repeatedly to claim general performance without an appropriate evaluation design.
Tie uncertainty to the next step
The process may request a better image, route a field to review or prevent an action until verification. Agree the rule with the relevant operating owner.
Define a review record that shows the original image region, proposed field value and final correction. Reviewers should not need to search the entire document for every uncertain extraction. This can make human checking more useful while preserving evidence of how the operational value was established.
GAT’s SME automation article examines the work left after automation. Field-level OCR evaluation makes that work visible and prevents easy text from concealing errors in the values the business actually depends on.
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