Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing

• URL: http://arxiv.org/abs/2606.01393v1
• Date: Sun, 31 May 2026 18:35:30 GMT
• Title: Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing
• Abstract summary: We introduce Dr. DocBench, a difficulty-aware benchmark for expert-level document parsing.<n>Dr. DocBench spans 52 BISAC domains and selects documents through failure-based sampling.<n>It contains 4,514 annotated pages from long documents averaging around 100 pages, with 65k high-quality annotations for layout, reading order, hierarchical relations, and domain-specific visual contents.<n>Our analysis reveals substantial failures across subjects, content types, and structural attributes, highlighting Dr. DocBench as a comprehensive testbed for diagnosing and advancing document intelligence.
• Score: 53.41293908252118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Optical Character Recognition (OCR) and document parsing benchmarks are increasingly limited in coverage and difficulty: many focus on common document genres or uniformly sampled pages where modern parsers already perform strongly, while offering limited annotation for expert-domain structures such as chemical formula, music notation, complex tables, and cross-page layouts. We introduce Dr. DocBench, a difficulty-aware benchmark for expert-level document parsing. Built from a large-scale multilingual book corpus, Dr. DocBench spans 52 BISAC subject domains and selects challenging documents through parser-failure-based sampling, targeting cases where multiple state-of-the-art systems struggle. It contains 4,514 annotated pages from long documents averaging around 100 pages, with 65k high-quality page- and block-level annotations for layout, reading order, hierarchical relations, and domain-specific visual contents. Evaluations of pipeline-based parsers and general-purpose VLMs show that strong performance on existing benchmarks does not transfer to our expert-level document parsing. Our analysis reveals substantial failures across subjects, content types, and structural attributes, highlighting Dr. DocBench as a comprehensive testbed for diagnosing and advancing document intelligence.

Related papers

• DocPC: Document-Level Visual Retrieval via Representative Page Composition [18.502834715003928]
  We propose DocPC, a document-level visual retrieval framework based on Representative Page Composition.<n>We combine multi-positive contrastive learning with sparsely scheduled listwise optimization to handle multi-positive supervision prevalent at the document level.<n> DocPC-ColQwen achieves NDCG@5 of 44.09 on DocViRe, outperforming the strongest page-level baseline at 38.91 while reducing storage by 10.1x.
  arXiv  Detail & Related papers  (2026-08-26T06:46:44Z)
• XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understanding [66.6175918865927]
  XL-DocBench is a fully human-verified benchmark for extra-long document understanding.<n>1,519 retained questions from six professional domains and contexts up to 2,303 pages.<n>Each question has one of twelve reasoning labels, expert-annotated evidence pages, a typed verification rule, and an answer format.
  arXiv  Detail & Related papers  (2026-07-21T09:38:24Z)
• MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing [74.84107522458798]
  MPDocBench-Parse is a benchmark for multi-page document parsing in real-world applications.<n>It contains 433 manually annotated documents with 3,246 pages, covering 15 document types in English and Chinese, with diverse layout styles.
  arXiv  Detail & Related papers  (2026-05-21T07:36:41Z)
• Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA [71.42483000929614]
  Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents.<n>We propose Doc-$V*$, an textbfOCR-free agentic framework that casts multi-page DocVQA as sequential evidence aggregation.
  arXiv  Detail & Related papers  (2026-04-15T11:12:27Z)
• DISCO: Document Intelligence Suite for COmparative Evaluation [1.4425299138308667]
  Document intelligence requires accurate text extraction and reliable reasoning over document content.<n>We evaluate optical character recognition (OCR) pipelines and vision-language models (VLMs) separately on parsing and question answering across diverse document types.
  arXiv  Detail & Related papers  (2026-03-04T14:47:34Z)
• Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing [46.14775667559124]
  Document parsing from scanned images remains a significant challenge due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables.<n>Existing supervised fine-tuning methods often struggle to generalize across diverse document types, leading to poor performance, particularly on out-of-distribution data.<n>We introduce LayoutRL, a reinforcement learning framework that optimize layout understanding through composite rewards integrating normalized edit distance count accuracy, and reading order preservation.<n>We show that Infinity-Bench consistently achieves state-of-the-art performance across a broad range of document types, languages, and structural complexities.
  arXiv  Detail & Related papers  (2025-10-17T06:26:59Z)
• WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? [64.62909376834601]
  This paper introduces WildDoc, the inaugural benchmark designed specifically for assessing document understanding in natural environments.<n> evaluation of state-of-the-art MLLMs on WildDoc expose substantial performance declines and underscore the models' inadequate robustness compared to traditional benchmarks.
  arXiv  Detail & Related papers  (2025-05-16T09:09:46Z)
• OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations [22.336858733121158]
  We introduce OmniDocBench, a novel benchmark featuring high-quality annotations across nine document sources.<n>We conduct a thorough evaluation of both pipeline-based methods and end-to-end vision-language models.
  arXiv  Detail & Related papers  (2024-12-10T16:05:56Z)
• DocBank: A Benchmark Dataset for Document Layout Analysis [114.81155155508083]
  We present textbfDocBank, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents.
  arXiv  Detail & Related papers  (2020-06-01T16:04:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.