Classification of handwritten Japanese hiragana characters of 71 categories attached with sound marks by using pattern augmentation for deep learning

Yoshihiro Shima; Yuki Omori

doi:10.1117/12.2501991

26 July 2018 Classification of handwritten Japanese hiragana characters of 71 categories attached with sound marks by using pattern augmentation for deep learning

Yoshihiro Shima, Yuki Omori

Author Affiliations +

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 1082802 (2018) https://doi.org/10.1117/12.2501991
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

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 71 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 Alex- Net. The average error rate was 2.378% for 100 test patterns of each of the 71 classes for a 5-times test, and the lowest error rate was 2.113% with 468,600 training patterns of distorted hiragana characters. Experimental results showed that the proposed method is effective in recognizing handwritten hiragana characters.

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Yoshihiro Shima and Yuki Omori "Classification of handwritten Japanese hiragana characters of 71 categories attached with sound marks by using pattern augmentation for deep learning ", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 1082802 (26 July 2018); https://doi.org/10.1117/12.2501991

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