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Data Augmentation Based on CycleGAN for Improving Woodblock-Printing Mongolian Words Recognition

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

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Abstract

In order to improve the performance of woodblock printing Mongolian words recognition, a method based on cycle-consistent generative adversarial network (CycleGAN) has been proposed for data augmentation. A well-trained CycleGAN model can learn image-to-image translation without paired examples. To be specific, the style of machine printing word images can be transformed into the corresponding word images with the style of woodblock printing by utilizing a CycleGAN, and vice versa. In this way, new instances of woodblock printing Mongolian word images are able to be generated by using the two generative models of CycleGAN. Thus, the aim of data augmentation could be attained. Given a dataset of woodblock printing Mongolian word images, experimental results demonstrate that the performance of woodblock printing Mongolian words recognition can be improved through such the data augmentation.

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Acknowledgments

This study is supported by the Project for Science and Technology of Inner Mongolia Autonomous Region under Grant 2019GG281, the Natural Science Foundation of Inner Mongolia Autonomous Region under Grant 2019ZD14, the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region under Grant NJYT-20-A05, and the Natural Science Foundation of China under Grant 61463038 and 61763034.

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Authors and Affiliations

  1. School of Computer Science, Inner Mongolia University, Hohhot, 010021, China

    Hongxi Wei, Kexin Liu & Jing Zhang

  2. Provincial Key Laboratory of Mongolian Information Processing Technology, Hohhot, China

    Hongxi Wei, Kexin Liu & Jing Zhang

  3. National and Local Joint Engineering Research Center of Mongolian Information Processing Technology, Hohhot, China

    Hongxi Wei, Kexin Liu & Jing Zhang

  4. School of Electronic Information Engineering, Inner Mongolia University, Hohhot, China

    Daoerji Fan

Authors
  1. Hongxi Wei
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  2. Kexin Liu
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  3. Jing Zhang
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  4. Daoerji Fan
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Corresponding author

Correspondence to Hongxi Wei .

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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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Wei, H., Liu, K., Zhang, J., Fan, D. (2021). Data Augmentation Based on CycleGAN for Improving Woodblock-Printing Mongolian Words Recognition. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-86337-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86336-4

  • Online ISBN: 978-3-030-86337-1

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