Pattern augmentation for classifying handwritten japanese hiragana characters of 46 classes by using CNN pre-trained with object images
| Autor: | Michio Yasuda, Yumi Nakashima, Yuki Omori, Yoshihiro Shima |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
business.industry Computer science Feature vector Word error rate Pattern recognition 02 engineering and technology Hiragana Convolutional neural network Support vector machine 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Classifier (linguistics) 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICMV |
| DOI: | 10.1117/12.2522661 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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