A Character Attention Generative Adversarial Network for Degraded Historical Document Restoration Despite of recent breakthroughs in the accuracy of single character recognition using the deeper convolution neural networks, one of the remaining problems is that OCRs almost fail to recognize character patterns when they are severely degraded, especially those of the historical documents. Another problem to recognize characters in historical documents is the lack of sufficient training patterns because of the heavy cost for annotation. This paper proposes a character attention generative adversarial network named CAGAN for restoring heavily degraded character patterns in historical documents so that OCRs improve their accuracy and even help archeologists to decode them. The network is based on the UNet like architecture 1 with skip connections, and it is trained by the proposed loss function including the common adversarial loss (global loss) and the hierarchical character attentive loss (local loss). We made an experiment on 118 categories of most common Japanese Kanji characters, collected from severely damaged historical documents called Heijokyo mokkan written during the Nara period in Japan. The experiment shows that our method restores the shapes of characters and improves the recognition rate significantly, which is helpful for archeologists to decode damaged character patterns.

Improving Text Image Resolution using a Deep Generative Adversarial Network for Optical Character Recognition Optical character recognition (OCR) has been widely studied in previous work. Except for the models used, the recognition accuracy depends most on the resolution of the image to be recognized. To enhance OCR performance, this paper proposes an approach based on a generative adversarial network to improve text image resolution. Our approach uses a perceptual loss function that consists of an adversarial loss, a content loss and an L1 loss. The adversarial loss and the L1 loss are used to ensure the generated superresolved images are closer to the ground truth highresolution images. Meanwhile, the content loss is used to ensure the generated superresolved images and the input lowresolution images have similar features on the basis of perceptual instead of pixel similarity. To evaluate the proposed approach, we compare the recognition accuracies before and after improving the resolution of both English and Chinese text images. The results show that the recognition accuracies on the superresolved text images obtained with our approach are significantly higher than those on the lowresolution images without processing.