An OCR or document-extraction demo can look convincing while still failing on the fields that matter. A benchmark replaces visual impressions with a repeatable comparison between predicted values and known answers.
Define the fields before running the model
Create a schema for each document class. An invoice might require document number, issue date, seller, buyer, subtotal, tax, total, and status. Meeting minutes need a meeting date, attendees, decisions, and action items. If the target schema changes after looking at predictions, the score is no longer comparable.
Use more than one rendering condition
Text-layer PDFs test document parsing and field mapping. Image-only or scan-style PDFs also test OCR. Keeping matching document content across both conditions helps isolate whether errors come from character recognition or later extraction logic.
Normalize carefully
Decide which differences are formatting-only. Dates may be normalized to ISO format, currency may be compared as decimal values, and surrounding whitespace may be ignored. Do not normalize away meaningful errors such as the wrong identifier, missing line item, or incorrect sign.
Score at the field level
Document-level pass rates hide which fields fail. Record expected fields, exact matches, missing predictions, unexpected documents, and accuracy by document type and field name. A model with high overall accuracy may still be unusable if totals or invoice numbers fail frequently.
Preserve the benchmark version
Hash or version the PDFs, ground truth, schema, evaluator, and model settings. When prompts or models change, save the new result separately. This creates a regression history instead of replacing the only prior run.
A repeatable evaluation loop
- Run the extraction system against every benchmark PDF.
- Write one normalized prediction record per document.
- Evaluate predictions against untouched ground truth.
- Review missing documents and schema errors before interpreting accuracy.
- Group errors by document type, rendering condition, and field.
- Change one extraction component, rerun, and compare to the saved baseline.
Synthetic documents are not a complete substitute for an authorized real-world test corpus, but they provide a privacy-safe, shareable first benchmark whose answers are known in advance.