title: Document Parsing Unveiled: Techniques, Challenges, and Prospects for
    Structured Information Extraction
summary: 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_authors._id: 67207abcd9e8b7a75988e5a5
paper_authors.name: Qintong Zhang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/56675f2bdbfa2fb535495f15bf5eabd5.svg
paper_authors.fullname: Zhang Qintong
paper_authors.type: user
paper_authors.user: ZQTTTT
paper_authors._id: 67207abcd9e8b7a75988e5a6
paper_authors.name: Victor Shea-Jay Huang
paper_authors.status: claimed_verified
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paper_authors.fullname: Victor Shea-Jay Huang
paper_authors.type: user
paper_authors.user: jeix
paper_authors._id: 67207abcd9e8b7a75988e5a7
paper_authors.name: Bin Wang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672388558183-noauth.jpeg
paper_authors.fullname: Bin Wang
paper_authors.type: user
paper_authors.user: wanderkid
paper_authors._id: 67207abcd9e8b7a75988e5a8
paper_authors.name: Junyuan Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-04T09:46:54.080Z
paper_authors._id: 65411de281a8731a1c28293d
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paper_authors.fullname: junyuan
paper_authors.type: user
paper_authors.user: Carkham
paper_authors._id: 67207abcd9e8b7a75988e5a9
paper_authors.name: Zhengren Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T09:10:34.502Z
paper_authors._id: 66431358af62c6c266b921ad
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66431358af62c6c266b921ad/36VacSxNJSPM9Ap_35HSZ.jpeg
paper_authors.fullname: Wang
paper_authors.type: user
paper_authors.user: zr-wang
paper_authors._id: 67207abcd9e8b7a75988e5aa
paper_authors.name: Hao Liang
paper_authors._id: 67207abcd9e8b7a75988e5ab
paper_authors.name: Shawn Wang
paper_authors._id: 67207abcd9e8b7a75988e5ac
paper_authors.name: Matthieu Lin
paper_authors._id: 67207abcd9e8b7a75988e5ad
paper_authors.name: Wentao Zhang
paper_authors._id: 67207abcd9e8b7a75988e5ae
paper_authors.name: Conghui He
paper_title: Document Parsing Unveiled: Techniques, Challenges, and Prospects for
    Structured Information Extraction
paper_summary: 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_ai_summary: The survey reviews document parsing methodologies, including modular pipeline systems and end-to-end models, focusing on layout detection, content extraction, and multi-modal data integration, while highlighting challenges and future research directions.
paper_ai_keywords: layout detection content extraction text tables mathematical expressions multi-modal data integration large vision-language models