# Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model
## 4 Experiments
### Experimental Results
#### 4.2.1 Effectiveness of Our Model  
The rationality and validity of our model were thoroughly examined and confirmed in the experimental section. We proposed a semi-supervised model to alleviate the problem of incomplete Chinese structure in the generated results of training models based on unpaired data. The attention module was specifically enhanced to preserve character integrity and stroke smoothness. To validate our approach, we conducted experiments using six different fonts, designating the Regular Script font as the source font and the remaining five fonts as target style fonts.  
The experimental outcomes, depicted in Figure 6, illustrate the success of our proposed method in generating stylish Chinese fonts. The results demonstrate significant advancements in producing diverse styles of artistic Chinese characters that closely resemble the target fonts in terms of style and font structure. Despite the presence of some hyphenation in the target domain, the readability of the generated characters remains unaffected. Overall, our proposed model exhibits clear advantages in generating the five fonts considered in this study. It effectively preserves stroke details while acquiring the stylistic attributes of the target domain, resulting in improved accuracy and realism of the generated characters. These outcomes serve as compelling evidence of the efficacy of our method in producing stylish Chinese fonts.  
Both StrokeGAN and our proposed method were used as baselines during the training process, and the resulting rendering outcomes are depicted in Figure 7. Notably, the _Handwriting_ font generated by StrokeGAN exhibited noticeable stroke omissions and noise. Similarly, the _Hanyi Doll_ font generated by StrokeGAN suffered from significant stroke errors, leading to illegible fonts. Although the StrokeGAN-generated _Huawen Amber_ font had a small amount of stroke redundancy, it did not significantly affect font recognition. However, the other two types of generated fonts exhibited excessive stroke redundancy and noise, resulting in unreadable characters overall. In contrast, our approach effectively maintained the overall structure of Chinese characters while enhancing the coherence and fluidity of the strokes within the characters. Table 2 presents a comparison between the performance of our method and StrokeGAN in generating stylish fonts. Significantly, our method demonstrates substantial performance superiority over StrokeGAN.  
Figure 7: Comparison of our method with StrokeGAN for generating results on different fonts. The red rectangles mark some of the contrasts in the generated characters.  
Furthermore, we compared our proposed method with a model that includes a self-attention module to validate the plausibility of our improvements to the attention module, as shown in Figure 8. From the comparison, we can draw several conclusions. The model incorporating the self-attention module generates characters that closely resemble the target domain, with high readability and retention of most strokes in Chinese characters. However, it still faces the issue of missing strokes. In contrast, our method excels in Chinese font style migration and generates more realistic Chinese characters. Table 3 provides a performance comparison between our method and the model integrating the self-attention module in Chinese font generation. The results further highlight the advantages of our method. It not only achieves more accurate Chinese font generation but also overcomes the issue of missing strokes. These findings demonstrate the efficacy of our proposed approach in generating stylish Chinese fonts.