Occluded offline handwritten Chinese character recognition using deep convolutional generative adversarial network and improved GoogLeNet

In this paper, we propose a novel method for recognizing occluded offline handwritten Chinese characters based on deep convolutional generative adversarial network (DCGAN) and improved GoogLeNet. Different from previous methods, our proposed method is capable of inpainting and recognizing occluded characters without needing to know the concrete positions of corrupted regions. First, the generator and discriminator of DCGAN are combined to generate realistic Chinese characters from corrupted images, and the contextual loss and the content loss are further used to inpaint generated images. Finally, we use the improved GoogLeNet with traditional feature extraction methods to recognize the recovered handwritten Chinese characters. The proposed method is evaluated on the extended CASIA-HWDB1.1 dataset for two challenging inpainting tasks with different portions of blocks or random missing pixels. Experimental results show that our method can achieve higher repair rates and higher recognition accuracies than most of existing methods.