# Chinese Character Image Completion Using a Generative Latent Variable Model
## 1 Introduction  
As the technology for handling images has gradually developed, techniques for image completion have emerged [1; 2; 3; 4]. The techniques for image completion include object removal technology for erasing unnecessary objects in an image and image restoration technology for restoring corrupted images. The object detection technology is used to remove specific objects present in images [5; 6]. The object detection technology classifies objects in an image and detects their location [7; 8; 9; 10; 11; 12]. It is possible to delete an object by using a function to find a specific object or to remove unnecessary objects by leaving only the object. This method is suitable for use with simple images where the background of the image is not complex. Concerning the removal of objects in an image, not only the object detection technology but also image inpainting technologies have been developed [2; 3; 4]. This technology works by directly masking unnecessary objects in an image using a masking tool and naturally filling the removed area using an inpainting model; this has the advantage that it can be used even in images with complex backgrounds, and it is able to naturally fill corrupted areas and thus can also be used to restore corrupted images. The Chinese character image data covered in this paper comprise images extracted from ancient books. Ancient books have many corrupted characters due to poor storage conditions, and the gap between letters is not constant, and so unnecessary objects are often included in the process of extracting letters. Because these incomplete Chinese character images are difficult to use as data, image completion technology is required to make them usable images. Chinese characters can be transformed into characters with different meanings if the shape changes even slightly. Therefore, when restoring Chinesecharacter images, it is important to restore them to the correct shape. Image inpainting technologies are used to restore corrupted images to ensure high-quality images; however, if the corrupted area accounts for a large part of the overall image, it is difficult to restore it to the correct shape, which is not suitable for restoring corrupted Chinese character images. It is also not appropriate to use image inpainting technologies to remove unnecessary objects within Chinese character images. Chinese character images are black and white and very simple images; therefore, image inpainting technologies, which involve separately masking unnecessary objects using a masking tool, is not efficient. The object detection technology used to remove objects easily removes unnecessary objects from simple images such as Chinese character images. However, the unnecessary objects present in Chinese character images are part of other Chinese characters, and so they have very similar characteristics to necessary objects, reducing the ability to detect unnecessary objects. The variational autoencoder with classification (VAE-C) model proposed in this paper can remove unnecessary objects without separately masking them and without performance degradation, even if the necessary and unnecessary objects are similar. Furthermore, this model can accurately restore Chinese characters so that they are not altered when restoring corrupted characters. In addition, the noise in extracted Chinese character image was removed using fuzzy binarization [13,14]. The fuzzy binarization method minimizes information loss compared to the normal binarization method [1,2]. The VAE-C model is a generative model based on the VAE [15] that generates artificial data using a probability distribution learning method. The VAE has the strength of being advantageous in terms of generating new data in which features of input data are interpolated. Using the fact that images with the interpolated features can be outputted, we changed the VAE so that data with desired features could be outputted.