# CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)
###### Abstract  
Font generation using deep learning has made considerable progress using image style transfer, but the automatic conversion/generation of Chinese characters still remains a difficult task owing to the complex character shape and large number of Chinese characters. Most known Chinese character generation models use the image conversion method of the Chinese character shape itself; however, it is difficult to reproduce complex Chinese characters. Recent methods have utilized character compositionality by separating up to three or four components to improve the quality of generated characters, but it is still difficult to generate high-quality results for complex Chinese characters with many components. In this study, we proposed the CCFont model (component-based Chinese font generation model using generative adversarial networks (GANs)) that automatically generates all Chinese characters using Chinese character components (up to 17 components). The CCFont model generates all Chinese characters in various styles using the components of Chinese characters based on conditional GAN. By acquiring local style information from the components, the information is more accurate and there is less information loss than when global information is obtained from the image of the entire character, reducing the failure of style conversion and improving quality to produce high-quality results. Additionally, the CCFont model generates high-quality results without any additional training (zero-shot font generation without any additional training) for the first-seen characters and styles. For example, the CCFont model, which was trained with only traditional Chinese (TC) characters, generates high-quality results for languages that can be divided into components, such as Korean and Thai, as well as simplified Chinese (SC) characters that are only seen during inference. CCFont can be adopted as a multi-lingual font-generation model that can be applied to all languages, which can be divided into components. To the best of our knowledge, the proposed method is the first to generate a zero-shot multilingual generation model using components. Qualitative and quantitative experiments were conducted to demonstrate the effectiveness of the proposed method. Chinese font generation; radicals/components; GAN 2022  
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fonts for various languages has recently increased, and active research is being conducted on the generation of various characters, including Korean, Chinese, and English [2; 3; 4; 5]. Most font generation models use the generative adversarial network (GAN) [6; 7] as a basic frame, and various modified forms are used to achieve great results. However, in the case of Hangul and Hanja, which have a large number of characters and complex shapes, it is not easy to change their character style. This is because it is difficult to convert an image by acquiring style information from the content. In particular, it is difficult to successfully convert content images into complex Chinese characters [8; 9; 10]. Figure 1 shows a sample failure case for the generation of Chinese characters. To solve this problem, SKFont [11] proposed a three-stage network based on character image skeletonization [12] for generating Hangul font images. Additionally, the direct conversion of relatively simple components has also been attempted [13; 14; 15; 16; 17; 18; 19; 20; 21]. Recently, a component-based method for Hangul was introduced that generated high-quality results [20]. In Hangul, characters can be regenerated with a maximum of three components. In contrast, in Chinese characters, the number of components varies for each character, and many exceed 10 components. Therefore, a new model must be created to apply the component concepts to Chinese characters. In this study, we proposed a component-based Chinese font generation (CCFont) model that further separates Chinese character radicals into components and automatically generates high-quality Chinese characters using all components.