## 5 Experimental Results and Analyses
### Comparison Experiments and Results
The _SSIM_ is defined as:  
\[SSIM(u,v)=hum(u,v)con(u,v)str(u,v), \tag{17}\]  
where  
\[\left\{ \begin{array}{l}lum(u,v)=\frac{2\mu_{u}\mu_{v}+\gamma_{1}}{\mu_{v}^{ 2}+\mu_{v}^{2}+\gamma_{1}}\\ con(u,v)=\frac{2\mu_{v}\mu_{v}+\gamma_{2}}{\mu_{v}^{2}+\gamma_{2}^{2}+\gamma_{ 2}}\\ str(u,v)=\frac{\alpha_{uv}+\gamma_{1}}{\mu_{v}\alpha_{v}+\gamma_{3}}.\end{array} \right. \tag{18}\]  
The \(hum(u,v)\) is the luminance function which calculates the proximity between \(\mu_{u}\) and \(\mu_{v}\). Here the \(\mu_{u}\) and the \(\mu_{v}\) are the mean luminance of input two images. The \(con(u,v)\) is the contrast function which calculates the contrast of the input two images. Here the \(\sigma_{u}\) and the \(\sigma_{v}\) are the standard deviation of the input two images. The \(str(u,v)\) is the structure function which calculates the correlation  
Figure 9: The comparison results of fonts generated by various models. The images of the first row are the ground truth, and the second raw are generated by the Zi2 Zi model. Images of the third and fourth rows are generated by EG-GAN [1] and the EG-GAN model, respectively. of the input two images. Here the \(\sigma_{uv}\) is the covariance between two images. The positive constants \(\cap_{1}\), \(\cap_{2}\) and \(\cap_{3}\) are set to make sure that a non-null denominator. A combination of the obtained six fonts image and every single font image is employed in our experiment. Table 3 shows the comparison results of the SSIM and PSNR metrics between Zi2 Zi and our model in a combination of the obtained six fonts image. Table 4 shows the comparison results of the SSIM metric between Zi2 Zi and our model in every single font image, and Table 5 shows the comparison results of the PSNR metric between Zi2 Zi and our model in every single font image. In order to confirm that employing the EM Distance can improve the convergence rate of the model, the sampled comparison results from training process of Zi2 Zi and EG-GAN [1] are shown in Figure 10. We can see that our generated images have a higher quality and convergence rate in the same steps. Meanwhile, we compare the details of EG-GAN [1, 2] and EG-GAN [2] with EG-GAN [1] and EG-GAN, respectively. \"EG-GAN [1, 2]\" represents the EG-GAN model both without employing Gradient Penalty strategy and classification loss. \"EG-GAN [2]\" represents the EG-GAN model without employing classification loss. We notice that the classification strategy is useful, and the comparison results are shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Font 3** & **Font 5** & **Font 6** & **Font 7** & **Font 8** & **Font 9** \\ \hline Zi2 Zi & 0.8670 & 0.9006 & 0.9049 & 0.8820 & 0.8794 & 0.8809 \\ EG-GAN [1] & 0.8614 & 0.9036 & 0.9158 & 0.8732 & 0.8831 & 0.8835 \\ EG-GAN & 0.8690 & 0.9082 & 0.9092 & 0.8997 & 0.8940 & 0.8915 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Denotes EG-GAN without Gradient Penalty.  
\end{table} 
Table 4: Comparison of SSIM metrics for each single font. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **SSIM** & **PSNR** \\ \hline Zi2 Zi & 0.8854 & 14.7226 \\ EG-GAN [1] & 0.8865 & 14.7501 \\ EG-GAN & 0.8953 & 15.3035 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Denotes EG-GAN without Gradient Penalty.  
\end{table} 
Table 3: Comparison of objective image quality metrics (SSIM, PSNR) of the proposed model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Font 3** & **Font 5** & **Font 6** & **Font 7** & **Font 8** & **Font 9** \\ \hline Zi2 Zi & 14.2367 & 16.9022 & 16.3358 & 13.6608 & 13.7157 & 14.2021 \\ EG-GAN [1] & 14.1828 & 17.1461 & 17.1391 & 13.1789 & 14.0231 & 14.4951 \\ EG-GAN & 14.6932 & 17.3627 & 16.4064 & 14.8043 & 14.8671 & 15.0289 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Denotes EG-GAN without Gradient Penalty.  
\end{table} 
Table 5: Comparison of PSNR metrics for each single font. An evaluation experiment is conducted to verify the effectiveness of the proposed system.