Chinese Character Image Completion Using a Generative Latent Variable Model

Author:

Jo In-su,Choi Dong-bin,Park Young B.

Abstract

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Cross Auto-Encoder for Inscription Character Inpainting;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Cascading Blend Network for Image Inpainting;ACM Transactions on Multimedia Computing, Communications, and Applications;2023-08-25

3. Binary Inscription Character Inpainting Based on Improved Context Encoders;IEEE Access;2023

4. Progressively Inpainting Images Based on a Forked-Then-Fused Decoder Network;Sensors;2021-09-22

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