Style transfer network for complex multi-stroke text

Neural style transfer has achieved success in many tasks. It is also introduced to text style transfer, which uses a style image to generate transferred images with textures and shapes consistent with the semantic content of the reference image. However, when the text structure is complex, existing methods will encounter problems such as stroke adhesion and unclear text edges. This will affect the aesthetics of the generated image and bring a lot of extra workload to the designers. This paper proposes an improved text style transfer network for complex multi-stroke texts. We use shape-matching GAN as the baseline and perform the following modifications: (1) morphological methods, erosion and dilation, are introduced in image processing; (2) the SN-Resblock is added to the structure network, and a BCEWithLogits loss is added to the texture network; (3) AdaBelief optimizer is adopted to constrain the transfer of text structure. Further, a new dataset of traditional Chinese characters is constructed to train the model. Experimental results show that the proposed method outperforms state-of-the-art methods on both simple characters and complex multi-stroke characters. It is shown that our method increases the readability and aesthetics of the text.