THGAN: Generative Adversarial Network Based Transfer Learning for Historical Chinese Character Recognition Historical Chinese character recognition faces problems including low image quality and lack of labeled training samples. We propose a generative adversarial network (GAN) based transfer learning method to ease these problems. The proposed THGAN architecture includes a discriminator and a generator. The network structure of the discriminator is based on a convolutional neural network (CNN). Inspired by Wasserstein GAN, the loss function of the discriminator aims to measure the probabilistic distribution distance of the generated images and the target images. The network structure of the generator is a CNN based encoderdecoder. The loss function of the generator aims to minimize the distribution distance between the real samples and the generated samples. In order to preserve the complex glyph structure of a historical Chinese character, a weighted mean squared error (MSE) criterion by incorporating both the edge and the skeleton information in the ground truth image is proposed as the weighted pixel loss in the generator. These loss functions are used for joint training of the discriminator and the generator. Experiments are conducted on two tasks to evaluate the performance of the proposed THGAN. The first task is carried out on style transfer mapping for multifont printed traditional Chinese character samples. The second task is carried out on transfer learning for historical Chinese character samples by adding samples generated by THGAN. Experimental results show that the proposed THGAN is effective.

A Character Attention Generative Adversarial Network for Degraded Historical Document Restoration Despite of recent breakthroughs in the accuracy of single character recognition using the deeper convolution neural networks, one of the remaining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is that OCRs almost fail to recognize character patterns when they are severely degraded, especially those of the historical documents. Another problem to recognize characters in historical documents is the lack of sufficient training patterns because of the heavy cost for annotation.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a character attention generative adversarial network named CAGAN for restoring heavily degraded character patterns in historical documents so that OCRs improve their accuracy and even help archeologists to decode them.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.