KAGAN: A Chinese poetry style transfer method

Abstract

Character style transfer is challenging, especially when working with Chinese characters. Compared with English characters, Chinese characters have a range of structures and font styles and have attracted a lot of attention in recent research. Some GAN-based methods were proposed for Chinese character style transfer; however, these methods were focused on a single character image ignoring Chinese sentences or multiple characters in one image. A Chinese poetry style transfer method is proposed to address the problem based on Chinese character style transfer. The proposed method includes Smooth L1 loss, which is used to generate superior images. A novel key-attention mechanism generative adversarial network (KAGAN) and a multi-channel discriminator are introduced to generate high-quality images of Chinese characters. The experiments demonstrate that our method is better than other transfer methods, and the proposed model has improved nearly 2% from the conventional methods according to the SSIM evaluation metric.