Handwritten Chinese Character Blind Inpainting with Conditional Generative Adversarial Nets
Zhao Zhong, Fei Yin, Xu-Yao Zhang, Cheng‐Lin Liu · 2017
It is very common to use a regular grid like Tian-zi-ge or Mi-zi-ge to help writing in Chinese handwriting environment, especially in education and postal area. Although regular grid is helpful for writing, it is a disaster for recognition. This paper focuses on handwritten Chinese character blind inpainting with regular grid and spot. To solve this problem, we use the recently proposed conditional generative adversarial nets (GANs). Different from the traditional engineering based method like line detection or edge detection, conditional GANs learn a map between target and training data. The generator reconstructs character directly from the data and the discriminator guides the training process to make the generated character more realistic. In this paper, we can automatically remove regular grid in handwritten Chinese character and reconstruct the character's strokes correctly. Moreover, the evaluation on classification task achieved a near state-of-the-art performance on the simulation database and got a convincing result on real world regular grid handwritten Chinese character database.