Abstract
In Japan, there are vast amount of classical books written in cursive Japanese that cannot be read by modern people. It is difficult to recognize each cursive Japanese separately, because they are written connected. Furthermore, there are many types of shape of characters. Therefore, an efficient method to convert them into modern characters automatically is required. Some methods recognizing a block of a few characters using deep learning have been studied so far. However, every page in a Japanese historical book is stored by a photo image; therefore, it is desirable to recognize all characters in a photo image at once. In this paper, we propose a method using deep learning to recognize cursive Japanese in a photo image without separating a block of characters manually. Furthermore, we evaluate the performance of the proposed algorithm using photo images of an actual book.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shoten, I.: General Catalog of National Books. Iwanami Shoten, Chiyoda (2002)
Yamamoto, S., Osawa, T.: Labor saving for reprinting Japanese rare classical books: the development of the new method for OCR technology including Kana and Kanji characters in cursive style. Inf. Manag. 58(11), 819–826 (2016)
The Dataset of Kuzushiji (Open Data Center for Humanities). http://codh.rois.ac.jp/pmjt
Tarin, C., Mikel, B., Asanobu, K., Alex, L., Kazuaki, Y., David, H.: Deep learning for classical Japanese literature. In: Proceedings of 2018 Workshop on Machine Learning for Creativity and Design (Thirty-second Conference on Neural Information Processing Systems), 3 December 2018
21st PRUM Algorithm Contest. https://sites.google.com/view/alcon2017/prmu
National Institute of Japanese Literature. http://base1.nijl.ac.jp/infolib/metapub/CsvSearch.cg
Shibayama, M., et al.: Research on Higher Accuracy Document Character Recognition Systems (in Japanese). Grants-in-Aid for Scientific Research (B)(1) report on research results, pp. 33–49 (2005)
Hayasaka, T., Ohno, W., Kato, Y., Yamamoto, K.: Recognition of Hentaigana by deep learning and trial production of WWW application (in Japanese). In: Proceedings of IPSJ Symposium of Humanities and Computer Symposium, pp. 7–12 (2016)
Nguyen, H.T., Ly, N.T., Nguyen, K.C., Nguyen, C.T., Nakagawa, M.: Attempts to recognize anomalously deformed Kana in Japanese historical documents. In: Proceedings of 4th International Workshop on Historical Document Imaging and Processing, pp. 31–36, 10–11 November 2017
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations, San Diego, 7–9 May 2015
Acknowledgements
This work was partially supported by the Japan Society for the Promotion of Science through Grants-in-Aid for Scientific Research (B) (17H01742) and JST CREST JPMJCR1402.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sichao, L., Miwa, H. (2020). Algorithm Using Deep Learning for Recognition of Japanese Historical Characters in Photo Image of Historical Book. In: Barolli, L., Nishino, H., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2019. Advances in Intelligent Systems and Computing, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-29035-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-29035-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29034-4
Online ISBN: 978-3-030-29035-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)