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Pattern augmentation for classifying handwritten japanese hiragana characters of 46 classes by using CNN pre-trained with object images
Author(s): Yoshihiro Shima; Yuki Omori; Yumi Nakashima; Michio Yasuda
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Paper Abstract

Neural networks are powerful technology for classifying character patterns and object images. A huge number of training samples is very important for classification accuracy. A novel method for recognizing handwritten hiragana characters is proposed that combines pre-trained convolutional neural networks (CNN) and support vector machines (SVM). The training samples are augmented by pattern distortion such as by cosine translation and elastic distortion. A pre-trained CNN, Alex-Net, can be used as the pattern feature extractor. Alex-Net is pre-trained for large-scale object image datasets. An SVM is used as a trainable classifier. Original hiragana samples of 46 classes on the ETL9B are divided in two-fold by odd and even dataset numbers. Samples with the odd dataset number and augmented patterns on the ETL9B database are trained by the SVM. The feature vectors of character patterns are passed to the SVM from AlexNet. The average error rate was 1.130% for 100 test patterns of each of the 46 classes for a 5-times test, and the lowest error rate was 0.978% with 506138 training patterns of distorted hiragana characters. Experimental results showed that the proposed method is effective in recognizing handwritten hiragana characters.

Paper Details

Date Published: 15 March 2019
PDF: 11 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412G (15 March 2019); doi: 10.1117/12.2522661
Show Author Affiliations
Yoshihiro Shima, Meisei Univ. (Japan)
Yuki Omori, Meisei Univ. (Japan)
Yumi Nakashima, Meisei Univ. (Japan)
Michio Yasuda, Meisei Univ. (Japan)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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