Skip to main content
SpringerLink
Log in
Book cover

International Conference on Document Analysis and Recognition

ICDAR 2021: Document Analysis and Recognition – ICDAR 2021 pp 526–537Cite as

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

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12824))

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Wei, H., Gao, G.: A keyword retrieval system for historical Mongolian document images. Int. J. Doc. Anal. Recognit. (IJDAR) 17(1), 33–45 (2014). https://doi.org/10.1007/s10032-013-0203-6

    Article  Google Scholar 

  2. Gao, G., Su, X., Wei, H., Gong, Y.: Classical Mongolian words recognition in historical document. In: Proceedings of the 11th International Conference on Document Analysis and Recognition, pp. 692–697. IEEE (2011)

    Google Scholar 

  3. Su, X., Gao, G., Wei, H., Bao, F.: Enhancing the mongolian historical document recognition system with multiple knowledge-based strategies. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds.) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science, vol. 9490, pp. 536–544. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26535-3_61

  4. Su, X., Gao, G., Wei, H., Bao, F.: A knowledge-based recognition system for historical Mongolian documents. Int. J. Doc. Anal. Recognit. (IJDAR) 19(3), 221–235 (2016). https://doi.org/10.1007/s10032-016-0267-1

    Article  Google Scholar 

  5. Wei, H., Gao, G.: A holistic recognition approach for woodblock-print Mongolian words based on convolutional neural network. In: Proceedings of the 26th IEEE International Conference on Image Processing, pp. 2726–2730. IEEE (2019)

    Google Scholar 

  6. Kang, Y., Wei, H., Zhang, H., Gao, G.: Woodblock-printing Mongolian words recognition by BI-LSTM with attention mechanism. In: Proceedings of the 15th International Conference on Document Analysis and Recognition, pp. 910–915. IEEE (2019)

    Google Scholar 

  7. Wei, H., Gao, G., Bao, Y.: A method for removing inflectional suffixes in word spotting of Mongolian Kanjur. In: Proceedings of the 11th International Conference on Document Analysis and Recognition, pp. 88–92. IEEE (2011)

    Google Scholar 

  8. Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  9. Sangkloy, P., Lu, J., Fang, C., Yu, F., Hays, J.: Scribbler: controlling deep image synthesis with sketch and color. In: Proceedings of 2017 IEEE International Conference on Computer Vision and Pattern Recognition, pp. 5400–5409. IEEE (2017)

    Google Scholar 

  10. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of 2017 IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1125–1134. IEEE (2017)

    Google Scholar 

  11. Tian, Y.: Zi2zi: Master Chinese calligraphy with conditional adversarial networks (2017). https://github.com/kaonashi-tyc/zi2zi

  12. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of 2017 IEEE International Conference on Computer Vision, pp. 2223–2232. IEEE (2017)

    Google Scholar 

  13. Li, M., Huang, H., Ma, L., Liu, W., Zhang, T., Jiang, Y.: Unsupervised image-to-image translation with stacked cycle-consistent adversarial networks. In: Proceedings of 2018 European Conference on Computer Vision, pp. 184–199. IEEE (2018)

    Google Scholar 

  14. Zhu, X., Liu, Y., Li, J., Wan, T., Qin, Z.: Emotion classification with data augmentation using generative adversarial networks. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds.) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science, vol. 10939, pp. 349–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93040-4_28

  15. Shi, Z., Liu, M., Cao, Q., Ren, H., Luo, T.: A data augmentation method based on cycle-consistent adversarial networks for fluorescence encoded microsphere image analysis. Signal Process. 161, 195–202 (2019)

    Article  Google Scholar 

  16. Hammami, M., Friboulet, D., Kechichian, R.: Cycle GAN-based data augmentation for multi-organ detection in CT images via Yolo. In: Proceedings of the 28th International Conference on Image Processing, pp. 390–393. IEEE (2020)

    Google Scholar 

  17. Chang, B., Zhang, Q., Pan, S., Meng, L.: Generating handwritten Chinese characters using CycleGAN. In: Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision, pp. 199–207. IEEE (2018)

    Google Scholar 

  18. Wu, L., Chen, X., Meng, L., Meng, X.: Multitask adversarial learning for Chinese font style transfer. In: Proceedings of 2020 International Joint Conference on Neural Networks, pp. 1–8. IEEE (2020)

    Google Scholar 

  19. Farooqui, F.F., Hassan, M., Younis, M.S., Siddhu, M.K.: Offline hand written Urdu word spotting using random data generation. IEEE Access 8, 131119–131136 (2020)

    Article  Google Scholar 

  20. Zhang, H., Wei, H., Bao, F., Gao, G.: Segmentation-free printed traditional Mongolian OCR using sequence to sequence with attention model. In: Proceedings of the 14th International Conference on Document Analysis and Recognition, pp. 585–590. IEEE (2017)

    Google Scholar 

  21. Wei, H., Zhang, H., Zhang, J., Liu, K.: Multi-task learning based traditional Mongolian words recognition. In: Proceedings of the 25th International Conference on Pattern Recognition, pp. 1275–1281. IEEE (2021)

    Google Scholar 

  22. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

Download references

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.

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kexin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daoerji Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongxi Wei .

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

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86336-4

  • Online ISBN: 978-3-030-86337-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation