Recognition of Handwritten Japanese Characters Using Ensemble of
Convolutional Neural Networks
- URL: http://arxiv.org/abs/2306.03954v1
- Date: Tue, 6 Jun 2023 18:30:51 GMT
- Title: Recognition of Handwritten Japanese Characters Using Ensemble of
Convolutional Neural Networks
- Authors: Angel I. Solis, Justin Zarkovacki, John Ly and Adham Atyabi
- Abstract summary: The study used an ensemble of three convolutional neural networks (CNNs) for recognizing handwritten Kanji characters.
The results indicate feasibility of using proposed CNN-ensemble architecture for recognizing handwritten characters.
- Score: 0.17646262965516946
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Japanese writing system is complex, with three character types of
Hiragana, Katakana, and Kanji. Kanji consists of thousands of unique
characters, further adding to the complexity of character identification and
literature understanding. Being able to translate handwritten Japanese
characters into digital text is useful for data analysis, translation, learning
and cultural preservation. In this study, a machine learning approach to
analyzing and recognizing handwritten Japanese characters (Kanji) is proposed.
The study used an ensemble of three convolutional neural networks (CNNs) for
recognizing handwritten Kanji characters and utilized four datasets of MNIST,
K-MNIST, Kuzushiji-49 (K49) and the top 150 represented classes in the
Kuzushiji-Kanji (K-Kanji) dataset for its performance evaluation. The results
indicate feasibility of using proposed CNN-ensemble architecture for
recognizing handwritten characters, achieving 99.4%, 96.4%, 95.0% and 96.4%
classification accuracy on MNIST, K-MNIS, K49, and K-Kanji datasets
respectively.
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