# CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)
## 2 Related Works
### Few-Shot-Learning  
In a few-shot setting, the goal is to generate characters in an unseen font style with just a few reference characters of that style at the test time. Recent few-shot methods have tackled this font image generation by utilizing the composition of characters. For example, DM-Font [24] generates Hangul and Thai characters with very few reference characters. It extracts the component information of Hangul characters and stores it in memory and uses it whenever necessary. This takes a significant amount of time, and only Hangul stored in memory can be generated during the inference time. Another problem with DM-Font is that it cannot generate other language characters, such as Chinese. By improving the problem of DM-Font, LF-Font [25], which enables Chinese characters, uses a style encoder and a content encoder for each component to generate Korean and Chinese characters without fine-tuning. However, due to architecture constraints, LF-Font is unable to perform multi-lingual font style transfer.  
To overcome LF-Font issues, MX-Font [26] proposed multilingual font generation architecture by utilizing multi-head encoders for each reference image to separate information between content and style, enabling work across languages. It has superior performance to DM-Font and LF-Font and produces high-quality results; however, training a character for the first time requires repeated exposure. Recently, RD-GAN [17] was proposed based on Chinese character radicals for zero shot Chinese character style transfer based on radical decompositions. However, RD-GAN is unable to produce unseen font styles.  
More recently, a fine-grained local style from reference style characters is extracted [19]. However, because of the complex structure, such as more than 200 components of Chinese characters, only experimental results using certain components were presented, and limited performance was shown for complex characters. CKFont [20] pairs the source character with the individual components of the target character, obtains style information from the individual components of the target character, and generates results. As a result, it is possible to expand and apply not only Hangul but also Chinese and Thai characters, as it generates all the characters by training to separate and combine components, and it produces high-quality results in a short time.  
Obtaining component images from a character image is extremely limited and challenging when the character's shape is complex. We resolved this issue by separating the component from the shape of the character and then recombining the transformed component image by converting the separated component image, yielding positive results. Style information and content structure are the most important factors in glyph style transfer. Obtaining accurate style information and continuously transmitting information without any loss are critical for image reproduction. To overcome such style information acquisition and loss, we developed the proposed CCFont model that separates the complex shapes of Chinese characters into components (decomposition), extracts local styles from these components separately, and generates characters of the target style by recombining the extracted component (composition) features. The trained model, in this way, reconstructs the characters in the introduced method, regardless of the content of the components. Therefore, it worked successfully for all Chinese characters (TC and SC) that could be separated into components, as well as Hangul and Thai characters. In other words, the CCFont model is a new multi-lingual (or cross-lingual) font generation model [26] of the zero-shot concept [15; 17] that works even with characters and styles that the model sees for the first time.