UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis

• URL: http://arxiv.org/abs/2503.15893v2
• Date: Wed, 26 Mar 2025 02:59:45 GMT
• Title: UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis
• Authors: Jiawei Wang, Kai Hu, Qiang Huo,
• Abstract summary: We propose a unified relation prediction approach for HDSA, called UniHDSA.<n>UniHDSA treats various HDSA sub-tasks as relation prediction problems and consolidates relation prediction labels into a unified label space.<n>To validate the effectiveness of UniHDSA, we develop a multimodal end-to-end system based on Transformer architectures.
• Score: 7.057192434574117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning [39.10966524559436]
  Document image segmentation is crucial for document analysis and recognition. Existing methods address these tasks separately, resulting in limited generalization and resource wastage. This paper introduces DocSAM, a transformer-based unified framework designed for various document image segmentation tasks.
  arXiv  Detail & Related papers  (2025-04-05T07:14:53Z)
• Graph-based Document Structure Analysis [26.79096546002763]
  We propose a novel graph-based Document Structure Analysis (gDSA) task.<n>This task requires that model not only detects document elements but also generates spatial and logical relations in form of a graph structure.<n>We construct a relation graph-based document structure analysis dataset (GraphDoc) with 80K document images and 4.13M relation annotations.
  arXiv  Detail & Related papers  (2025-02-04T17:16:14Z)
• CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks [0.0]
  We present CAISSON, a novel hierarchical approach to Retrieval-Augmented Generation (RAG)<n>At its core, CAISSON leverages dual Self-Organizing Maps (SOMs) to create complementary organizational views of the document space.<n>To evaluate CAISSON, we develop SynFAQA, a framework for generating synthetic financial analyst notes and question-answer pairs.
  arXiv  Detail & Related papers  (2024-12-03T21:00:10Z)
• HDT: Hierarchical Document Transformer [70.2271469410557]
  HDT exploits document structure by introducing auxiliary anchor tokens and redesigning the attention mechanism into a sparse multi-level hierarchy. We develop a novel sparse attention kernel that considers the hierarchical structure of documents.
  arXiv  Detail & Related papers  (2024-07-11T09:28:04Z)
• DLAFormer: An End-to-End Transformer For Document Layout Analysis [7.057192434574117]
  We propose an end-to-end transformer-based approach for document layout analysis, called DLAFormer. We treat various DLA sub-tasks as relation prediction problems and consolidate these relation prediction labels into a unified label space. We introduce a novel set of type-wise queries to enhance the physical meaning of content queries in DETR.
  arXiv  Detail & Related papers  (2024-05-20T03:34:24Z)
• Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis [9.340346869932434]
  We propose a tree construction based approach that addresses multiple subtasks concurrently. We present an effective end-to-end solution based on this framework to demonstrate its performance. Our end-to-end system achieves state-of-the-art performance on two large-scale document layout analysis datasets.
  arXiv  Detail & Related papers  (2024-01-22T12:00:37Z)
• Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs [79.0426838808629]
  We propose TAT-DQA, i.e. to answer the question over a visually-rich table-text document. Specifically, we propose a novel Doc2SoarGraph framework with enhanced discrete reasoning capability. We conduct extensive experiments on TAT-DQA dataset, and the results show that our proposed framework outperforms the best baseline model by 17.73% and 16.91% in terms of Exact Match (EM) and F1 score respectively on the test set.
  arXiv  Detail & Related papers  (2023-05-03T07:30:32Z)
• HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures [31.868926876151342]
  This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. We built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. We propose an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem.
  arXiv  Detail & Related papers  (2023-03-24T07:23:56Z)
• Unified Pretraining Framework for Document Understanding [52.224359498792836]
  We present UDoc, a new unified pretraining framework for document understanding. UDoc is designed to support most document understanding tasks, extending the Transformer to take multimodal embeddings as input. An important feature of UDoc is that it learns a generic representation by making use of three self-supervised losses.
  arXiv  Detail & Related papers  (2022-04-22T21:47:04Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Multilevel Text Alignment with Cross-Document Attention [59.76351805607481]
  Existing alignment methods operate at a single, predefined level. We propose a new learning approach that equips previously established hierarchical attention encoders for representing documents with a cross-document attention component.
  arXiv  Detail & Related papers  (2020-10-03T02:52:28Z)
• DocBank: A Benchmark Dataset for Document Layout Analysis [114.81155155508083]
  We present textbfDocBank, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents.
  arXiv  Detail & Related papers  (2020-06-01T16:04:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.