Character recognition of modern Japanese official documents using CNN for imbalanced learning data

Zongjhe Yang; Keisuke Doman; Masashi Yamada; Yoshito Mekada

doi:10.1117/12.2521307

22 March 2019 Character recognition of modern Japanese official documents using CNN for imbalanced learning data

Zongjhe Yang, Keisuke Doman, Masashi Yamada, Yoshito Mekada

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 1104906 (2019) https://doi.org/10.1117/12.2521307
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

Citation Download Citation

Zongjhe Yang, Keisuke Doman, Masashi Yamada, and Yoshito Mekada "Character recognition of modern Japanese official documents using CNN for imbalanced learning data", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 1104906 (22 March 2019); https://doi.org/10.1117/12.2521307

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available