Poster
in
Workshop: The Third Workshop on Long-Context Foundation Models

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

Minglai Yang ⋅ Xinyan Yu ⋅ Pengyuan Li ⋅ Xinyu Guo ⋅ Zhenting Qi ⋅ Konwoo Kim ⋅ Longtian Ye ⋅ Xiaolong Luo ⋅ Jinhe Bi ⋅ Henry Zhang ⋅ Haris Riaz ⋅ Ruby Zhang ⋅ Yunze Xiao ⋅ Bangya Liu ⋅ Tom Tang ⋅ Yunfei Zhao ⋅ Qunshu Lin ⋅ Zihan Wang ⋅ Minghao Liu ⋅ Michael Lingzhi Li ⋅ Yilun Du ⋅ Jesse Thomason ⋅ Rogerio Feris ⋅ Alex Pentland ⋅ Zexue He

Project Page [ OpenReview]

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.

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