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The Japanese writing system is complex, with three character types ofHiragana, Katakana, and Kanji. Kanji consists of thousands of uniquecharacters, further adding to the complexity of character identification andliterature understanding. Being able to translate handwritten Japanesecharacters into digital text is useful for data analysis, translation, learningand cultural preservation. In this study, a machine learning approach toanalyzing and recognizing handwritten Japanese characters (Kanji) is proposed.The study used an ensemble of three convolutional neural networks (CNNs) forrecognizing handwritten Kanji characters and utilized four datasets of MNIST,K-MNIST, Kuzushiji-49 (K49) and the top 150 represented classes in theKuzushiji-Kanji (K-Kanji) dataset for its performance evaluation. The resultsindicate feasibility of using proposed CNN-ensemble architecture forrecognizing handwritten characters, achieving 99.4%, 96.4%, 95.0% and 96.4%classification accuracy on MNIST, K-MNIS, K49, and K-Kanji datasetsrespectively.

Recognition of Handwritten Japanese Characters Using Ensemble of Convolutional Neural Networks