Papers › VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations

VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations

13 May 2021arXiv:2105.06220archive 2025-07-28

Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, ShiLiang Pu, Yi Niu, Fei Wu

Document layout analysis is crucial for understanding document structures. On this task, vision and semantics of documents, and relations between layout components contribute to the understanding process. Though many works have been proposed to exploit the above information, they show unsatisfactory results. NLP-based methods model layout analysis as a sequence labeling task and show insufficient capabilities in layout modeling. CV-based methods model layout analysis as a detection or segmentation task, but bear limitations of inefficient modality fusion and lack of relation modeling between layout components. To address the above limitations, we propose a unified framework VSR for document layout analysis, combining vision, semantics and relations. VSR supports both NLP-based and CV-based methods. Specifically, we first introduce vision through document image and semantics through text embedding maps. Then, modality-specific visual and semantic features are extracted using a two-stream network, which are adaptively fused to make full use of complementary information. Finally, given component candidates, a relation module based on graph neural network is incorported to model relations between components and output final results. On three popular benchmarks, VSR outperforms previous models by large margins. Code will be released soon.

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Code

hikopensource/davar-lab-ocr officialpytorchApache-2.0 report

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Tasks

Document Layout AnalysisGraph Neural Network

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Document Layout Analysis PubLayNet val VSR Figure 0.964 #3 of 15 Archive leaderboard report
Document Layout Analysis PubLayNet val VSR List 0.947 #3 of 15 Archive leaderboard report
Document Layout Analysis PubLayNet val VSR Overall 0.957 #3 of 15 Archive leaderboard report
Document Layout Analysis PubLayNet val VSR Table 0.974 #3 of 15 Archive leaderboard report
Document Layout Analysis PubLayNet val VSR Text 0.967 #3 of 15 Archive leaderboard report
Document Layout Analysis PubLayNet val VSR Title 0.931 #3 of 15 Archive leaderboard report

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Methods

Graph Neural Network

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