A generative model for Chinese character style fusion based on generative adversarial networks
Lvqing Zhou, Zhiqiang Dong, Xiongkai Chen · 2023
Generative adversarial networks (GANs) have achieved remarkable success in image translation tasks. However, existing models and theories are still limited in their ability to generate new fonts that combine the styles of both font domains. Most current models only perform style conversion, which often results in generated fonts that are biased towards the target font domain, such as Chinese characters. Moreover, the long training time of these models makes them impractical for real-world applications. To address these issues, we propose a novel approach for font generation based on the pix2pix and AC-GAN frameworks. Our model can learn the mapping from the source font domain X to the target font domain Y without biasing the generated fonts towards either font domain. During training, the generator is optimized to produce high-quality new fonts that combine the styles of both font domains, while the discriminator is trained to distinguish between real and generated fonts. Experimental results show that our approach can generate 2555 new fonts with both font styles from a training set of 943 characters in a short time. The generated fonts are visually appealing and combine the styles of both font domains, achieving the goal of fusing styles and generating new fonts.