How to Actually Benchmark OCR Accuracy (Instead of Trusting a Vendor's Number)
"99% accurate" means very different things depending on how it's measured. Here's how to ask the right question before you sign a contract.
Almost every document processing vendor will tell you their OCR is "99% accurate." It's rarely a lie — but it's also rarely the number you think it is, because accuracy can be measured at three completely different levels, and they don't produce the same result.
Character-level accuracy measures whether individual letters were read correctly. It's the easiest number to make look good, because a single wrong digit in a 12-digit account number still counts as "91% correct" at the character level — even though that field is now completely useless. Word-level accuracy is a step better, but still doesn't tell you whether the fields that matter to your business — an invoice total, a policy number, a date of birth — came out right.
The number that actually matters is field-level accuracy on your document types, benchmarked against a manually keyed "ground truth" sample. That means: take a real batch of your documents, have a human key them by hand, run the same batch through the OCR/IDP pipeline, and compare field by field. Ask any vendor for this specific number, on your document types, not an average across every client they've ever had.
A few other questions worth asking before you trust a headline accuracy figure: Is the number before or after human validation? (TRILO's pipeline reports both — raw AI confidence and final human-verified accuracy — because they answer different questions.) Does it hold up on your actual scan quality, not a vendor's clean sample set? And critically: what happens to the fields that fall below the confidence threshold? A good answer is "routed to a trained analyst before delivery." A bad answer is "delivered anyway."