DOCR-Inspector: Fine-Grained and Automated Evaluation of Document Parsing with VLM

• URL: http://arxiv.org/abs/2512.10619v1
• Date: Thu, 11 Dec 2025 13:16:33 GMT
• Title: DOCR-Inspector: Fine-Grained and Automated Evaluation of Document Parsing with VLM
• Authors: Qintong Zhang, Junyuan Zhang, Zhifei Ren, Linke Ouyang, Zichen Wen, Junbo Niu, Yuan Qu, Bin Wang, Ka-Ho Chow, Conghui He, Wentao Zhang,
• Abstract summary: Document parsing aims to transform unstructured PDF images into semi-structured data, facilitating the digitization and utilization of information in diverse domains.<n>Common practice often selects the top-performing model on standard benchmarks.<n>We introduce DOCR-Inspector, which formalizes document parsing assessment as fine-grained error detection and analysis.
• Score: 35.910677096654574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document parsing aims to transform unstructured PDF images into semi-structured data, facilitating the digitization and utilization of information in diverse domains. While vision language models (VLMs) have significantly advanced this task, achieving reliable, high-quality parsing in real-world scenarios remains challenging. Common practice often selects the top-performing model on standard benchmarks. However, these benchmarks may carry dataset-specific biases, leading to inconsistent model rankings and limited correlation with real-world performance. Moreover, benchmark metrics typically provide only overall scores, which can obscure distinct error patterns in output. This raises a key challenge: how can we reliably and comprehensively assess document parsing quality in the wild? We address this problem with DOCR-Inspector, which formalizes document parsing assessment as fine-grained error detection and analysis. Leveraging VLM-as-a-Judge, DOCR-Inspector analyzes a document image and its parsed output, identifies all errors, assigns them to one of 28 predefined types, and produces a comprehensive quality assessment. To enable this capability, we construct DOCRcase-200K for training and propose the Chain-of-Checklist reasoning paradigm to enable the hierarchical structure of parsing quality assessment. For empirical validation, we introduce DOCRcaseBench, a set of 882 real-world document parsing cases with manual annotations. On this benchmark, DOCR-Inspector-7B outperforms commercial models like Gemini 2.5 Pro, as well as leading open-source models. Further experiments demonstrate that its quality assessments provide valuable guidance for parsing results refinement, making DOCR-Inspector both a practical evaluator and a driver for advancing document parsing systems at scale. Model and code are released at: https://github.com/ZZZZZQT/DOCR-Inspector.

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