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Improved Graph Methods for Table Layout Understanding

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12822))

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Abstract

Recently, there have been significant advances in document layout analysis and, particularly, in the recognition and understanding of tables and other structured documents in handwritten historical texts. In this work, a series of improvements over current techniques based on graph neural networks are proposed, which considerably improve state-of-the-art results. In addition, a two-pass approach is also proposed where two graph neural networks are sequentially used to provide further substantial improvements of more than 12 F-measure points in some tasks. The code developed for this work will be published to facilitate the reproduction of the results and possible improvements.

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Notes

  1. 1.

    https://github.com/JoseRPrietoF/TableUnderstanding_ICDAR21

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Acknowledgments

Work partially supported by the Universitat Politècnica de València under grant FPI-I/SP20190010 (Spain). Work partially supported by the BBVA Foundation through the 2018–2019 Digital Humanities research grant “HistWeather – Dos Siglos de Datos Climáticos” and also supported by the Generalitat Valenciana under the EU- FEDER Comunitat Valenciana 2014–2020 grant ”Sistemas de fabricación inteligente para la industria 4.0”, by Ministerio de Ciencia, Innovación y Universidades project DocTIUM (Ref. RTI2018-095645-B-C22) and by Generalitat Valenciana under project DeepPattern (PROMETEO/2019/121)

Computing resources were provided by the EU-FEDER Comunitat Valenciana 2014–2020 grant IDIFEDER/2018/025.

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Authors and Affiliations

  1. PRHLT Research Center, Universitat Politècnica de València, Valencia, Spain

    Jose Ramón Prieto & Enrique Vidal

Authors
  1. Jose Ramón Prieto
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  2. Enrique Vidal
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Corresponding author

Correspondence to Jose Ramón Prieto .

Editor information

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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Prieto, J.R., Vidal, E. (2021). Improved Graph Methods for Table Layout Understanding. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_33

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  • DOI: https://doi.org/10.1007/978-3-030-86331-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86330-2

  • Online ISBN: 978-3-030-86331-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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