title: UniHDSA: A Unified Relation Prediction Approach for Hierarchical
    Document Structure Analysis
summary: Document structure analysis, aka document layout analysis, is crucial for
  understanding both the physical layout and logical structure of documents,
  serving information retrieval, document summarization, knowledge extraction,
  etc. Hierarchical Document Structure Analysis (HDSA) specifically aims to
  restore the hierarchical structure of documents created using authoring
  software with hierarchical schemas. Previous research has primarily followed
  two approaches: one focuses on tackling specific subtasks of HDSA in isolation,
  such as table detection or reading order prediction, while the other adopts a
  unified framework that uses multiple branches or modules, each designed to
  address a distinct task. In this work, we propose a unified relation prediction
  approach for HDSA, called UniHDSA, which treats various HDSA sub-tasks as
  relation prediction problems and consolidates relation prediction labels into a
  unified label space. This allows a single relation prediction module to handle
  multiple tasks simultaneously, whether at a page-level or document-level
  structure analysis. To validate the effectiveness of UniHDSA, we develop a
  multimodal end-to-end system based on Transformer architectures. Extensive
  experimental results demonstrate that our approach achieves state-of-the-art
  performance on a hierarchical document structure analysis benchmark,
  Comp-HRDoc, and competitive results on a large-scale document layout analysis
  dataset, DocLayNet, effectively illustrating the superiority of our method
  across all sub-tasks. The Comp-HRDoc benchmark and UniHDSA's configurations are
  publicly available at https://github.com/microsoft/CompHRDoc.
paper_authors._id: 67e55e19c7f04e09b025a261
paper_authors.name: Jiawei Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T14:25:07.694Z
paper_authors._id: 64060b49a577649430bf6974
paper_authors.avatarUrl: /avatars/74d0d6ed656b593e4c101b09edf18c7a.svg
paper_authors.fullname: Jiawei Wang
paper_authors.type: user
paper_authors.user: Jarvis1111
paper_authors._id: 67e55e19c7f04e09b025a262
paper_authors.name: Kai Hu
paper_authors._id: 67e55e19c7f04e09b025a263
paper_authors.name: Qiang Huo
paper_title: UniHDSA: A Unified Relation Prediction Approach for Hierarchical
    Document Structure Analysis
paper_summary: Document structure analysis, aka document layout analysis, is crucial for
  understanding both the physical layout and logical structure of documents,
  serving information retrieval, document summarization, knowledge extraction,
  etc. Hierarchical Document Structure Analysis (HDSA) specifically aims to
  restore the hierarchical structure of documents created using authoring
  software with hierarchical schemas. Previous research has primarily followed
  two approaches: one focuses on tackling specific subtasks of HDSA in isolation,
  such as table detection or reading order prediction, while the other adopts a
  unified framework that uses multiple branches or modules, each designed to
  address a distinct task. In this work, we propose a unified relation prediction
  approach for HDSA, called UniHDSA, which treats various HDSA sub-tasks as
  relation prediction problems and consolidates relation prediction labels into a
  unified label space. This allows a single relation prediction module to handle
  multiple tasks simultaneously, whether at a page-level or document-level
  structure analysis. To validate the effectiveness of UniHDSA, we develop a
  multimodal end-to-end system based on Transformer architectures. Extensive
  experimental results demonstrate that our approach achieves state-of-the-art
  performance on a hierarchical document structure analysis benchmark,
  Comp-HRDoc, and competitive results on a large-scale document layout analysis
  dataset, DocLayNet, effectively illustrating the superiority of our method
  across all sub-tasks. The Comp-HRDoc benchmark and UniHDSA's configurations are
  publicly available at https://github.com/microsoft/CompHRDoc.
paper_ai_summary: A unified relation prediction approach using Transformer architectures achieves state-of-the-art performance in hierarchical document structure analysis and competitive results in document layout analysis.
paper_ai_keywords: Hierarchical Document Structure Analysis HDSA relation prediction Transformer architectures Comp-HRDoc DocLayNet