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http://dx.doi.org/10.6109/jkiice.2020.24.3.355

Training Method for Enhancing Classification Accuracy of Kuzushiji-MNIST/49 using Deep Learning based on CNN  

Park, Byung-Seo (Department of Electronic Material Engineering, Kwangwoon University)
Lee, Sungyoung (Ingenium College of Liberal Arts, Kwangwoon University)
Seo, Young-Ho (Department of Electronic Material Engineering, Kwangwoon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.3, 2020 , pp. 355-363 More about this Journal
Abstract
In this paper, we propose a deep learning training method for accurately classifying Kuzushiji-MNIST and Kuzushiji-49 datasets for ancient and medieval Japanese characters. We analyze the latest convolutional neural network networks through experiments to select the most suitable network, and then use the networks to select the number of training to classify Kuzushiji-MNIST and Kuzushiji-49 datasets. In addition, the training is conducted with high accuracy by applying learning methods such as Mixup and Random Erase. As a result of the training, the accuracy of the proposed method can be shown to be high by 99.75% for MNIST, 99.07% for Kuzushiji-MNIST, and 97.56% for Kuzushiji-49. Through this deep learning-based technology, it is thought to provide a good research base for various researchers who study East Asian and Western history, literature, and culture.
Keywords
convolutional neural network; deep learning; Kuzushiji-MNIST; training; classification;
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