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  "text": "Document OCR benchmarks are still an open problem\n\nExisting document OCR benchmarks are either too narrowly focused on a specific type (e.g. FinTabNet, ChartQA), or on documents that aren’t reflective of real-world tasks (e.g. OmniDocBench, OlmOCR-bench on over academic papers)\n\nParseBench is a step towards solving this problem. \n* It tries to comprehensively cover real-world document distributions within the enterprise.\n* It contains comprehensive evaluations across 5 different dimensions (tables, charts, content faithfulness, formatting, grounding). \n* It tries to use metrics that optimize for agent semantic understanding rather than structural similarity.\n\nWe released this yesterday, and there’s a TON of content:\n1. Whitepaper\n2. HF dataset\n3. Github repo\n4. Blog\n5. Video\n\nAnd today, we’re excited to feature https://t.co/FYbk3s6M2w, our home page website for ParseBench 💫 come check it out!\n\nTake a look at some of our other materials if you’re interested:\nBlog: https://t.co/57OHkx0pQW\nPaper: https://t.co/Ho2oH2xEAM",
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