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Character recognition of modern Japanese official documents using CNN for imbalanced learning data
Author(s): Zongjhe Yang; Keisuke Doman; Masashi Yamada; Yoshito Mekada
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Paper Abstract

The documents of the government-general of Taiwan recorded from 1895 to 1945 contain the whole of Japanese official documents before the end of the WW2, and have great historic value. The characters in the documents, however, are illegible because they were written by hand with a brush. It is labor-intensive work for historians or scholars to understand the documents. We propose a method for character recognition of these documents by using a convolutional neural network and also conduct to solve the problem of imbalanced learning data. Experimental results show that the top-1 and the top10 accuracies were 89.48% and 98.10%, respectively.

Paper Details

Date Published: 22 March 2019
PDF: 4 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 1104906 (22 March 2019); doi: 10.1117/12.2521307
Show Author Affiliations
Zongjhe Yang, Chukyo Univ. (Japan)
Keisuke Doman, Chukyo Univ. (Japan)
Masashi Yamada, Chukyo Univ. (Japan)
Yoshito Mekada, Chukyo Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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