OCR & Extraction Benchmark Framework for Insurance Documents

Standard OCR benchmarks — DocVQA, SROIE, FUNSD — were built for document understanding tasks, not insurance extraction. They measure whether a model can answer questions about a document. That's not the problem. The problem is whether a VIN extracted from a scanned police report is byte-perfect, whether the same document produces the same output on every run, and whether that output can be traced back to a specific location in the source when a regulator asks.

The framework below is what we actually use to evaluate tools. We share it because the evaluation criteria are as important as the results — and because vendors who cherry-pick generic benchmarks are counting on you not asking the right questions.

How to use this framework

• 1 Use Groups 1–3 to shortlist tools in a proof-of-concept. These dimensions separate tools that handle insurance document complexity from those that only perform on clean digital inputs.
• 2 Use Group 4 to validate operational fit for your document volume and deployment model — in-premise vs. cloud, GPU vs. CPU, latency requirements for real-time FNOL workflows.
• 3 Treat Group 5 as a binary gate — pass or disqualify. No partial credit. A tool that hallucinates field values, even occasionally, is not deployable in a regulated insurance workflow regardless of performance on Groups 1–4.