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International Conference on Document Analysis and Recognition

ICDAR 2021: Document Analysis and Recognition – ICDAR 2021 pp 432–447Cite as

Annotation-Free Character Detection in Historical Vietnamese Stele Images

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

Abstract

Images of Historical Vietnamese stone engravings provide historians with a unique opportunity to study the past of the country. However, due to the large heterogeneity of thousands of images regarding both the text foreground and the stone background, it is difficult to use automatic document analysis methods for supporting manual examination, especially with a view to the labeling effort needed for training machine learning systems. In this paper, we present a method for finding the location of Chu Nom characters in the main text of the steles without the need of any human annotation. Using self-calibration, fully convolutional object detection methods trained on printed characters are successfully adapted to the handwritten image collection. The achieved detection results are promising for subsequent document analysis tasks, such as keyword spotting or transcription.

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Notes

  1. 1.

    https://vietnamica.hypotheses.org.

  2. 2.

    Readers interested in the source code and the dataset are referred to our GitHub repository https://github.com/asciusb/annotationfree.

  3. 3.

    http://www.nomfoundation.org.

  4. 4.

    More specifically, three random selections have been performed. 11 samples have been selected among already transcribed steles, 22 samples from the dataset used in previous work [20], and 22 samples from the rest of the dataset.

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Acknowledgements

This work has been supported by the Swiss Hasler Foundation (project 20008). It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 833933 - VIETNAMICA).

We would like to thank Bélinda Hakkar, Marine Scius-Bertrand, Jean-Michel Nafziger, René Boutin, Morgane Vannier, Delphine Mamie and Tobias Widmer for annotating bounding boxes during more than hundred hours to create the ground truth of the test set.

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Authors and Affiliations

  1. iCoSys, University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Anna Scius-Bertrand, Michael Jungo, Beat Wolf & Andreas Fischer

  2. Ecole Pratique des Hautes Etudes, PSL, Paris, France

    Anna Scius-Bertrand & Marc Bui

  3. DIVA, University of Fribourg, Fribourg, Switzerland

    Andreas Fischer

Authors
  1. Anna Scius-Bertrand
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  2. Michael Jungo
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  3. Beat Wolf
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  4. Andreas Fischer
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  5. Marc Bui
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Corresponding author

Correspondence to Anna Scius-Bertrand .

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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Scius-Bertrand, A., Jungo, M., Wolf, B., Fischer, A., Bui, M. (2021). Annotation-Free Character Detection in Historical Vietnamese Stele Images. 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_28

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  • DOI: https://doi.org/10.1007/978-3-030-86549-8_28

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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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