Dimensions overall score 7.0

• MDPBench comprises 3,400 document images spanning 17 languages, diverse scripts, and varied photographic conditions.method 90%
• MDPBench is the first benchmark for multilingual digital and photographed document parsing.method 95%
• Existing document parsing benchmarks predominantly focus on digital-born and scanned documents in limited languages, leading to model bias.limitation 85%
• Open-source document parsing models suffer a dramatic performance collapse on non-Latin scripts and real-world photographed documents, with an average drop of 17.8% on photographed documents and 14.0% on non-Latin scripts.result 90%
• Closed-source models (notably Gemini3-Pro) prove relatively robust compared to open-source alternatives on the MDPBench evaluation.result 85%
• The annotation pipeline for MDPBench combines automatic labeling by expert models with manual correction and human verification to ensure high quality.method 90%
• Photographed documents in MDPBench were captured under diverse real-world conditions including indoor/outdoor scenes, physical deformation, and varying camera orientations.method 85%
• Current document parsing models have key limitations including challenges with photographed documents, limited recognition of non-Latin scripts, and language-specific reading order issues.limitation 80%

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