Recognition of Handwritten Japanese Characters Using Ensemble of Convolutional Neural Networks
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.
Keywords
Cite
@article{arxiv.2306.03954,
title = {Recognition of Handwritten Japanese Characters Using Ensemble of Convolutional Neural Networks},
author = {Angel I. Solis and Justin Zarkovacki and John Ly and Adham Atyabi},
journal= {arXiv preprint arXiv:2306.03954},
year = {2023}
}