Abstract
In this paper, we propose a method to generate handwritten style characters by extracting the characteristics of a person’s handwritten characters. We use DCGAN (Deep Convolutional Generative Adversarial Networks) to generate handwritten style characters, which preserves the unique characteristics of a person’s handwritten characters. Some methods to generate handwritten style characters have been proposed so far. However, there are too many Kanji characters to prepare handwritten characters as training data; therefore, it is impossible to generate handwritten style characters by extracting the characteristics of a person’s handwritten characters including Kanji characters. In facts, since there are 2,136 Kanji characters for regular use, these would require a large amount of training time and training effort. Our method generates characters that have never been written before by decomposing and extracting Kanji characters into elements, learns each element separately, generates each element, and merges them. Therefore, the system implemented by the method can learn from a limited number of handwritten characters and automatically generate characters that do not exist in the training data, and can generate characters that preserve the characteristics of a person’s handwritten characters. Thus, the number of handwritten style characters that can be generated automatically can be significantly increased.
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Shimomura, H., Miwa, H. (2023). Automatic Generation of Handwritten Style Characters Including Untrained Characters. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_2
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