Components Regulated Generation of Handwritten Chinese Text-lines in Arbitrary Length

Shuo Li, Xiyan Liu, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Abstract: Generating readable images of handwritten Chinese text-lines is very challenging due to complicated topological structures in Chinese. To address this problem, we propose a components regulated model named HCT-GAN to generate the entire lines of Chinese handwriting from text-line labels. Specifically, HCT-GAN is designed as a CGAN-based architecture that additionally integrates a Chinese text encoder (CTE), a sequence recognition module(SRM), and a spatial perception module (SPM). Compared with the one-hot embedding, CTE learns the latent content representation by reusing the structure and component embedding shared among the Chinese characters. SRM provides sequence-level constraints to the generated images. SPM can adaptively constrain the spatial correlation between the generated components, which facilitates the modeling of characters with complicated topological structures. Benefiting from such artful modeling, our model suffices to generate images of handwritten Chinese text-lines in arbitrary length. Extensive experimental results demonstrate that our model achieves state-of-the-art performance in handwritten Chinese lines generation.