mlsanityData to 7 Oct 2026

READoc: A Unified Benchmark for Realistic Document Structured Extraction

arXiv 2409.05137 ↗First posted 8 Sep 2024cs.CL cs.CVcited by 15

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Abstract (arXiv)

Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequate, significantly hindering the field's advancement. This problem is largely attributed to existing benchmark paradigms, which exhibit fragmented and localized characteristics. To address these limitations and offer a thorough evaluation of DSE systems, we introduce a novel benchmark named READoc, which defines DSE as a realistic task of converting unstructured PDFs into semantically rich Markdown. The READoc dataset is derived from 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo. In addition, we develop a DSE Evaluation S3uite comprising Standardization, Segmentation and Scoring modules, to conduct a unified evaluation of state-of-the-art DSE approaches. By evaluating a range of pipeline tools, expert visual models, and general VLMs, we identify the gap between current work and the unified, realistic DSE objective for the first time. We aspire that READoc will catalyze future research in DSE, fostering more comprehensive and practical solutions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 33 of its 49 references that are in the map. First posted on arXiv 8 Sep 2024.

Builds on

33 of 49 references.

References · 33

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Citing papers per year: 2024: 2; 2025: 1; 2026: 12.

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  1. 2024Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction
  2. 2022VRDU: A Benchmark for Visually-rich Document Understanding
  3. 2026NovaLAD: A Fast, CPU-Optimized Document Extraction Pipeline for Generative AI and Data Intelligence
  4. 2026Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing
  5. 2026Document Retrieval-Aware Chunking (D-RAC): Universal Retrieval-Aware Ingestion of Enterprise Documents via PDF Normalization and Multimodal Markdown Conversion
  6. 2025DOCR-Inspector: Fine-Grained and Automated Evaluation of Document Parsing with VLM
  7. 2026Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents
  8. 2026Evaluating Structured Information Extraction with Open Models in a High Risk Public Sector Application