# Chinese Character Image Completion Using a Generative Latent Variable Model
## 3 Results
### Object Removal Performance Comparison  
Chinese character datasets were used to test the efficacy of object removal. The Places2, CelebA, and Cifar-10 datasets, which were tested earlier, are complex images, unlike Chinese character data, which are black and white images. The image inpainting method is used to remove complex image objects, and this technology uses the method of forcibly damaging and restoring the area of the object that is intended to be removed within the image. This paper conducted an experiment to remove objects without the process of forcibly damaging the area of the object within the image with simple images such as Chinese character images.  
In simple images, such as Chinese character images, object detection techniques are sometimes used to remove unnecessary objects that exist within the images [5; 6]. However, there is a problem in that the performance of object detection technology is degraded because the unnecessary objects present within the Chinese character images have similar characteristics to the desired object. Figure 13 compares the qualitative results of removing unnecessary objects in the image using VAE-C models and the object detection technology mask regions with convolutional neural networks (Mask R-CNN) [10]. The result of removing unnecessary objects using Mask R-CNN shows that all unnecessary objects were not removed or that the required objects were corrupted. In contrast, the VAE-C model neatly removed only unnecessary objects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Methods** & **MSE \(\downarrow\)** & **PSNR \(\uparrow\)** & **SSIM [38]**\(\uparrow\) \\ \hline \multirow{3}{*}{Places2 [39]} & Pconv & 9.43 & 38.38 db & 94.85\% \\  & Gconv & 13.06 & 36.97 db & 91.70\% \\  & VAE-C & 8.23 & 38.97 db & 98.21\% \\ \hline \multirow{3}{*}{CelebA [40]} & Pconv & 17.99 & 35.81 db & 90.20\% \\  & Gconv & 21.6 & 34.79 db & 86.46\% \\  & VAE-C & 11.21 & 37.63 db & 98.84\% \\ \hline \multirow{3}{*}{Cifar-10} & Pconv & 3.87 & 42.24 db & 99.59\% \\  & Gconv & 29.89 & 33.37 db & 83.58\% \\  & VAE-C & 2.48 & 44.17 db & 99.81\% \\ \hline \multirow{3}{*}{Chinese character} & Pconv & 6.71 & 39.87 db & 93.75\% \\  & Gconv & 10.17 & 38.06 db & 90.07\% \\ \cline{1-1}  & VAE-C & 2.6 & 43.97 db & 99.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: A table comparing the degree of restoration of corrupted areas when restored using PConv, GConv, and VAE-C models with Places2, CelebA (celebrity faces attributes) face, Cifar-10, and Chinese character images dataset. MSE: mean square error; PSNR: peak signal-to-noise ratio; SSIM: structural similarity index measure.  
Figure 13: Comparison of the results of eliminating unnecessary objects by using the VAE-C model and Mask R-CNN (mask regions with convolutional neural networks).  
Table 3 shows the degree to which Mask R-CNN and VAE-C models removed unnecessary objects using a similarity comparison scale. The VAE-C model performed well with a PSNR of 5.2, SSIM of 9.7, and MSE of 8.8 compared to Mask R-CNN. This result shows numerically that the VAE-C model is more efficient than the Mask R-CNN for eliminating unnecessary objects.