OCR evals done right: custom annotation apps and agent citation checkers to catch silent errors
HamelHusain · x · 2026-10-12
Isaac Flath walks through his daily process for evaluating OCR models and document-question-answering agents, arguing teams often tune prompts or upgrade models without realizing the error source is the OCR layer. Using a food inspection report as an example, he shows how harmless differences (underlines) must be separated from fatal factual errors (wrong point values), and catalogs common OCR failure modes: oversimplified tables, swapped digits, ignored watermarks, and bold headers collapsed into the wrong cell. He pairs custom annotation apps for side-by-side error labeling with agent citation checkers to verify answers actually come from the document.
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