Handwritten Chinese Character Generation via Embedding, Decomposition and Discrimination
Yuanyuan Zhang, Yonghong Song, Anqi Li · 2023
Handwritten Chinese characters generation (HCCG) aims to automatically generate handwritten Chinese character images with various styles, which is very important for training a high performance handwritten Chinese character recognition model. Most existing HCCG methods take Chinese characters as a whole and ignore their structures and details, resulting in the lack of fine-grained representations and monotonous styles, which further affects the generalization ability of handwritten Chinese character recognition model. In this paper, we propose a novel HCCG method, named Embedding, Decomposition and Discrimination Network (EDDNet), which decomposes a Chinese character into a component sequence to represent its internal structures and radicals, so as to achieve the diversity of writing styles through fine-grained representations of handwritten Chinese characters. First, we propose a local style embedding module (LSEM) to inject target styles into content features. Then we decompose characters into sequences of components and propose a fine-grained content discriminator (FCD) to maintain the content integrity of generated images. Extensive experiments demonstrate that EDDNet can generate handwritten Chinese character images with more perfect details and realistic writing styles. Moreover, it is easy to extend to unseen styles and unseen characters.