From the beginning of the 1950s, Chinese mainland officials on the basis of traditional Chinese characters was a simplified form of Chinese writing new standard, simplified Chinese, and traditional Chinese characters before simplifying called traditional characters.Īt present, Traditional Chinese, mainly in China Taiwan region, Hong Kong Special Administrative Region, the Macao Special Administrative Region, and North America use other overseas Chinese communities section.
Experiments demonstrate that our method has a state-of-the-art generalization ability in few-shot Chinese character style transfer.Traditional Chinese is one of the Chinese fonts used by the Chinese nation and has a history of more than 2000 years. By doing so, only a few target Chinese calligraphy characters are needed to generated expected style transferred characters. To capture valuable style knowledge in target and strengthen the coarse-grained understanding of character content, we utilize multiple unpaired samples to align the feature distributions belonging to different character styles. To be specific, a few paired samples from different character styles are leveraged to attain a fine-grained correlation between structures underlying different glyphs. In this paper, we propose a simple but powerful end-to-end Chinese calligraphy font generation framework ZiGAN, which does not require any manual operation or redundant preprocessing to generate fine-grained target-style characters with few-shot references. Recently, several GAN-based methods have been proposed for font synthesis, but some of them require numerous reference data and the other part of them have cumbersome preprocessing steps to divide the character into different parts to be learned and transferred separately.
Moreover, the handwriting of calligraphy masters has a more irregular stroke and is difficult to obtain in real-world scenarios. Chinese character style transfer is a very challenging problem because of the complexity of the glyph shapes or underlying structures and large numbers of existed characters, when comparing with English letters.
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