UniHDSA: A Unified Relation Prediction Approach for Hierarchical
  Document Structure Analysis
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.
67e55e19c7f04e09b025a261
Jiawei Wang
claimed_verified
2025-03-27T14:25:07.694Z
64060b49a577649430bf6974
/avatars/74d0d6ed656b593e4c101b09edf18c7a.svg
Jiawei Wang
user
Jarvis1111
67e55e19c7f04e09b025a262
Kai Hu
67e55e19c7f04e09b025a263
Qiang Huo
UniHDSA: A Unified Relation Prediction Approach for Hierarchical
  Document Structure Analysis
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.
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.
Hierarchical Document Structure Analysis HDSA relation prediction Transformer architectures Comp-HRDoc DocLayNet