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Adaptive Scaling for Archival Table Structure Recognition

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12821))

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Abstract

Table detection and structure recognition from archival document images remain challenging due to diverse table structures, complex document layouts, degraded image qualities and inconsistent table scales. In this paper, we propose an instance segmentation based approach for archival table structure recognition which utilizes both foreground cell content and background ruling line information. To overcome the influence from inconsistent table scales, we design an adaptive image scaling method based on average cell size and density of ruling lines inside each document image. Different from previous multi-scale training and testing approaches which usually slow down the speed of the whole system, our adaptive scaling resizes each image to a single optimal size which can not only improve overall model performance but also reduce memory and computing overhead on average. Extensive experiments on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method.

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Notes

  1. 1.

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Acknowledgments

This work has been supported by the National Key Research and Development Program Grant 2020AAA0109702, the National Natural Science Foundation of China (NSFC) grants 61733007, 61721004.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, People’s Republic of China

    Xiao-Hui Li, Fei Yin, Xu-Yao Zhang & Cheng-Lin Liu

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Xiao-Hui Li, Xu-Yao Zhang & Cheng-Lin Liu

  3. CAS Center for Excellence of Brain Science and Intelligence Technology, Beijing, People’s Republic of China

    Cheng-Lin Liu

Authors
  1. Xiao-Hui Li
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  2. Fei Yin
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  3. Xu-Yao Zhang
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  4. Cheng-Lin Liu
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Corresponding author

Correspondence to Cheng-Lin Liu .

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Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Li, XH., Yin, F., Zhang, XY., Liu, CL. (2021). Adaptive Scaling for Archival Table Structure Recognition. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12821. Springer, Cham. https://doi.org/10.1007/978-3-030-86549-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-86549-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86548-1

  • Online ISBN: 978-3-030-86549-8

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