What this guide helps you evaluate
Teams evaluating OCR and AI extraction for invoices, forms, contracts or semi-structured documents.
Prepared by the PROFENG Editorial & Engineering Team. 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.
How to use and verify this guide
Use the comparison criteria and questions as a decision framework, then verify current prices, legal requirements, technical specifications, service terms and regulatory obligations with the primary or official references listed above. PROFENG explains its source, calculation and review principles on the methodology page.