Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction

Zhang Qintong, Victor Shea-Jay Huang, Bin Wang, junyuan, Zhengren Wang, Hao Liang, Shawn Wang, Matthieu Lin, Wentao Zhang, Conghui He

Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction: 30 upvotes on Hugging Face Daily Papers, #5 of 21 papers on 2024-10-29. Day-by-day upvote history.

Document parsing is essential for converting unstructured and semi-structured documents-such as contracts, academic papers, and invoices-into structured, machine-readable data. Document parsing extract reliable structured data from unstructured inputs, providing huge convenience for numerous applications. Especially with recent achievements in Large Language Models, document parsing plays an indispensable role in both knowledge base construction and training data generation. This survey presents a comprehensive review of the current state of document parsing, covering key methodologies, from modular pipeline systems to end-to-end models driven by large vision-language models. Core components such as layout detection, content extraction (including text, tables, and mathematical expressions), and multi-modal data integration are examined in detail. Additionally, this paper discusses the challenges faced by modular document parsing systems and vision-language models in handling complex layouts, integrating multiple modules, and recognizing high-density text. It emphasizes the importance of developing larger and more diverse datasets and outlines future research directions.

Paper page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.