DocPTBench: Benchmarking End-to-End Photographed Document Parsing and Translation

• URL: http://arxiv.org/abs/2511.18434v1
• Date: Sun, 23 Nov 2025 13:02:11 GMT
• Title: DocPTBench: Benchmarking End-to-End Photographed Document Parsing and Translation
• Abstract summary: We introduce DocPTBench, a benchmark specifically designed for Photographed Document Parsing and Translation.<n>Our experiments demonstrate that transitioning from digital-born to photographed documents results in a substantial performance decline.<n>This substantial performance gap underscores the unique challenges posed by documents captured in real-world conditions.
• Score: 18.531174868051558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of Multimodal Large Language Models (MLLMs) has unlocked the potential for end-to-end document parsing and translation. However, prevailing benchmarks such as OmniDocBench and DITrans are dominated by pristine scanned or digital-born documents, and thus fail to adequately represent the intricate challenges of real-world capture conditions, such as geometric distortions and photometric variations. To fill this gap, we introduce DocPTBench, a comprehensive benchmark specifically designed for Photographed Document Parsing and Translation. DocPTBench comprises over 1,300 high-resolution photographed documents from multiple domains, includes eight translation scenarios, and provides meticulously human-verified annotations for both parsing and translation. Our experiments demonstrate that transitioning from digital-born to photographed documents results in a substantial performance decline: popular MLLMs exhibit an average accuracy drop of 18% in end-to-end parsing and 12% in translation, while specialized document parsing models show significant average decrease of 25%. This substantial performance gap underscores the unique challenges posed by documents captured in real-world conditions and reveals the limited robustness of existing models. Dataset and code are available at https://github.com/Topdu/DocPTBench.

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