Font Style Transfer Method Based on Improved Conditional Generative Adversarial Network
This paper introduces an approach for Chinese character font generating based on con-ditional generative adversarial networks,addressing the prevalent issues associated with sluggish convergence and intricate font structure handling found in conventional techniques.This method employs a generator trained by conditional generative adversarial networks as the core component of the font style transfer network.The method introduces a knowledge distillation technique that assimilates feature information from a pre-trained image reconstruction network to decode fea-tures with greater precision into target style fonts.Additionally,this method significantly bolsters the quality of generated target fonts by implementing a combination of edge smoothing loss and perceptual loss.This paper conducts a comprehensive analysis,both quantitatively and qualita-tively,comparing various fonts to existing font generation algorithms.Experimental results con-clusively demonstrate that the method generates more realistic target fonts with clearer defined character edges.
font style transferknowledge distillationconditional generative adversarial networks
万方数据
维普
