What this guide helps you evaluate
Teams evaluating OCR and AI extraction for invoices, forms, contracts or semi-structured documents.
This page is designed to help you compare the moving parts, organize due diligence and ask better questions before you commit money, sign a contract or change an operating process.
What to compare first
- Printed text, handwriting and low-quality scan accuracy
- Table, checkbox and layout extraction
- Prebuilt models versus trainable custom schemas
- API latency, throughput, human review and confidence scores
- Data residency, retention, security and per-page cost
Step-by-step process
- 01
Assemble a representative test set including difficult documents.
- 02
Define field-level accuracy metrics before testing vendors.
- 03
Measure extraction, normalization and exception-handling separately.
- 04
Test privacy controls and deletion behavior with security teams.
- 05
Calculate total cost including review labor and failed-document handling.
Common mistakes and risk checks
- Testing only clean sample documents.
- Reporting overall OCR accuracy when only a few fields matter operationally.
- Ignoring the cost and workflow of human review.