# Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model
## 2 Related Work  
Over the past few years, most of the applications used for generating Chinese fonts have been based on machine learning methods [1; 9]. These methods initially analyze the font's skeleton structure to extract stroke and radical information, which is then utilized to compose new Chinese characters; however, they have some limitations. With the widespread use of deep learning, numerous deep-learning approaches have emerged for generating stylish Chinese fonts [10; 11; 12]. For example, [PERSON] et al. [13] utilized the pix2 pix model, originally introduced by [PERSON] et al. [3], to generate Chinese fonts. They also introduced the zi2 zi method, which utilizes paired training data, where each character in the source style domain corresponds to a character in the target style domain during training. [PERSON] et al. [14] expanded on this concept by generating Chinese characters from a specific font to multiple diverse fonts. CalliGAN [15] also took advantage of the prior knowledge of Chinese character structure to handle the multi-class image-to-image conversion, i.e., multiple fonts to multiple font generation. [PERSON] et al. [16] improved the quality of generated results by employing a decoupling mechanism that separates character images into style representation and content representation. This approach effectively synthesized artistic text with specified properties, resulting in improved output quality. Additionally, various methods have been proposed for Chinese font generation, all of which rely on paired training data [17; 18; 19; 20; 21; 22]. With the advancement of deep learning, deep neural networks have made significant progress in extracting valuable feature information from Chinese characters, encompassing strokes, radicals, and structures. These features can be efficiently integrated into GAN models, offering vital supervisory information [23; 24; 25; 26]. However, collecting paired training data can be a time- and effort-consuming process. To tackle this challenge, [PERSON] et al. [6] utilized the CycleGAN method developed by [PERSON] et al. [2], enabling image style migration based on unpaired training data for Chinese font generation. Nevertheless, font style transformations often necessitate a substantial number of training samples. To overcome this limitation, [PERSON] et al. [27] focused on generating new fonts with a limited number of examples, leveraging the combined representation of radicals, parts, and strokes of Chinese characters to depict the glyph's style. [PERSON] et al. [28] utilized attention modules within style encoders to extract both shallow and deep style features. In addition, novel linguistic complexity-aware jump connections are employed to generate Chinese characters. Moreover, ZiGAN [29] was trained simultaneously on a reduced number of samples, facilitating Chinese font generation. Regarding training methods using unpaired data, AGT-GAN [30] employed a combination of global and local glyph style modeling to generate characters. This approach incorporated stroke-aware texture transfer and utilized adversarial learning mechanisms to achieve improved results. The CycleGAN-based approach proposed by [PERSON] et al. [6] is prone to the mode collapse problem, where the value domain of successive mappings becomes highly concentrated on a specific connected branch. To address this issue, StrokeGAN [7]incorporated explicit stroke feature information during the training phase of Chinese font generation. The stroke information can capture the mode information of Chinese characters and preserve the character's integrity, effectively mitigating the mode collapse problem. In response to the still-frequent problem of mode collapse, [PERSON] et al. [31] proposed a few-shot semi-supervised scheme to deal with these problems. Nevertheless, despite mitigating the mode collapse challenge, certain issues persist concerning the overall structure of Chinese characters and stroke fluency. Unlike natural images, Chinese character images exhibit a high degree of structural complexity.