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Xinwen Qi

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Proceedings Article | 30 November 2022 Paper

Generating handwritten Chinese characters of diversity based on GAN

Xinwen Qi, Jun Jiang, Peng Gu, Xufang Zhang, Changliang He

Proceedings Volume 12456, 1245629 (2022) https://doi.org/10.1117/12.2659328

KEYWORDS: Demodulation, Image processing, Image enhancement, Data modeling, Associative arrays

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At present, people pay less attention to the diversity of the generated results about the generation of handwritten Chinese characters. The structure of Chinese characters is complex and the process of handwriting has strong freedom. So the image of handwritten Chinese characters has a certain diversity. In this paper, a handwritten Chinese character generation adversarial network is proposed. By adding the standard font information and feature vector into the network, the generation results of the network can not only achieve certain accuracy but also produce diversity. Improved loss function makes the network more inclined to generate a diversity of results. The method using the connected domain segmentation is used to better ensure the accuracy of the generated image. By training on the handwritten Chinese character dataset, it is verified that the network shows good diversity when the accuracy is similar to that of other methods.

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