Choose the unit you are measuring
Character recognition, field extraction and accepting a whole document are different tasks. A readable name does not make a wrong identifier acceptable. Define the required fields, normalization rules and any critical fields before scoring a system.
Our driver-license OCR work connected structured extraction with operations review. The method below is a reusable evaluation approach; it does not claim measured accuracy or time savings for that client delivery.
Four measures that belong together
| Measure | How to calculate or inspect it | What it tells you |
|---|---|---|
| Field correctness | Correct expected fields ÷ all expected fields; count missing output as incorrect. | Whether the values are right, including difficult fields. |
| Review rate | Documents routed to a person ÷ documents evaluated. | How much manual handling the workflow requires. |
| False acceptance | Incorrect documents accepted automatically ÷ documents accepted automatically. | The risk hidden by a low review rate. If none are accepted, report N/A. |
| Completed-task effort | Time and cost through extraction, review and downstream completion. | Whether the full workflow improves on the current process. |
Test the inputs that break the workflow
Use representative examples with a known expected result. Keep test data approved for the environment and avoid placing real identity documents in public demos. The downloadable matrix contains synthetic scenario descriptions only; it is a planning template, not a benchmark dataset or a claim that a model passed.
| Scenario | Expected behavior |
|---|---|
| Clear, complete input | Extract the expected fields and apply the configured review policy. |
| Blurred or clipped identifier | Mark the field unresolved and route for correction. |
| Missing required page or field | Report incomplete input; do not invent the missing value. |
| Conflicting name or date | Preserve the conflict for a reviewer. |
| Duplicate upload or downstream timeout | Avoid duplicate records and expose a recoverable state. |
| Instructions embedded in the document | Treat them as document content, not authority to change the task. |
Make review an observable part of the product
A reviewer should see the source, the proposed values and the reason for review. Keep the original and corrected values distinguishable. Record whether the reviewer accepted, corrected or rejected the output so later analysis can identify recurring problems.
Confidence scores need calibration against your examples. A high score does not establish correctness by itself. Set review thresholds from observed errors and the consequence of a wrong value, and reassess them when the document mix or extraction system changes.
Carry the same checks into a controlled release
Freeze a small regression set and run it when changing the model, prompt, parser or validation rules. Add newly observed failure cases without losing the earlier ones. Evaluate the downstream integration too: a correct field sent to the wrong record is still a failed task.
Begin with the agreed review policy and monitor what operators actually correct. Expand automatic handling only when the evidence supports that decision for the relevant document types.
Continue exploring
- Download the synthetic evaluation matrix (.csv)
- Document OCR case study
- LuckyTruck document and data engineering
