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Link to original content: https://doi.org/10.1007/978-3-031-41734-4_12
A Hybrid Approach to Document Layout Analysis for Heterogeneous Document Images | SpringerLink
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A Hybrid Approach to Document Layout Analysis for Heterogeneous Document Images

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14191))

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Abstract

We present a new hybrid document layout analysis approach to simultaneously detecting graphical page objects, group text-lines into text regions according to reading order, and recognize the logical roles of text regions from heterogeneous document images. For graphical page object detection, we leverage a state-of-the-art Transformer-based object detection model, namely DINO, as a new graphical page object detector to detect tables, figures, and (displayed) formulas in a top-down manner. Furthermore, we introduce a new bottom-up text region detection model to group text-lines located outside graphical page objects into text regions according to reading order and recognize the logical role of each text region by using both visual and textual features. Experimental results show that our bottom-up text region detection model achieves higher localization and logical role classification accuracy than previous top-down methods. Moreover, in addition to the locations of text regions, our approach can also output the reading order of text-lines in each text region directly. The state-of-the-art results obtained on DocLayNet and PubLayNet demonstrate the effectiveness of our approach.

J. Wang, H. Sun, K. Hu and E. Zhang—This work was done when Jiawei Wang, Haiqing Sun, Kai Hu and Erhan Zhang were interns in MMI Group, Microsoft Research Asia, Beijing, China.

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Authors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Zhuoyao Zhong, Jiawei Wang, Haiqing Sun, Kai Hu, Erhan Zhang, Lei Sun & Qiang Huo

  2. Department of EEIS, University of Science and Technology of China, Hefei, China

    Jiawei Wang & Kai Hu

  3. School of Software and Microelectronics, Peking University, Beijing, China

    Haiqing Sun & Erhan Zhang

Authors
  1. Zhuoyao Zhong
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  2. Jiawei Wang
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  3. Haiqing Sun
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  4. Kai Hu
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  5. Erhan Zhang
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  6. Lei Sun
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  7. Qiang Huo
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Correspondence to Zhuoyao Zhong .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

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Zhong, Z. et al. (2023). A Hybrid Approach to Document Layout Analysis for Heterogeneous Document Images. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14191. Springer, Cham. https://doi.org/10.1007/978-3-031-41734-4_12

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  • DOI: https://doi.org/10.1007/978-3-031-41734-4_12

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