A Developer’s Guide to OmniParseBench: Leaderboard Benchmarks and OCR Testing
In this tutorial, we implement OmniParseBench, Datalab’s open benchmark for document parsers, which grades a parser’s output with 16,288 small pass-or-fail tests across 2,937 pages, from scanned forms and handwriting to arXiv math and dense tables.
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Key points
- Rather than trust the leaderboard, we rebuild it from the verdicts Datalab released for 27 parsers, after feeding the scorer a hand-made page to see which mistakes flip a verdict.
- We then measure ranking certainty, slice it by document type, test whether the two models the card names as helping build tests behave differently on tests a model corrected, and run Tesseract and Indra OCR ourselves on a 50-page sample.
- Along the way, we find that the top two parsers are statistically tied, the leader changes on most slices, and one launch figure doesn’t match the released files.
- RESULTS = {} # step -> one-line result, for the summary table
Sources (1)
- [1]A Developer’s Guide to OmniParseBench: Leaderboard Benchmarks and OCR TestingMarkTechPost · Oct 22, 04:11 PM
In this tutorial, we implement OmniParseBench, Datalab’s open benchmark for document parsers, which grades a parser’s output with 16,288 small pass-or-fail tests across 2,937 pages, from scanned forms and handwriting to arXiv math and dense tables.
Rather than trust the leaderboard, we rebuild it from the verdicts Datalab released for 27 parsers, after feeding the scorer a hand-made page to see which mistakes flip a verdict.
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