A Survey on Deep Learning-Based Chinese Font Style Transfer
Zheyong Ren, Yuhan Pan, Jieyan Chen, Lin Zhao, Ming Liao, Xuecheng Qian, Wei Gong · IEEE Transactions on Artificial Intelligence · 2025
In recent years, deep learning-based Chinese font style transfer has garnered extensive research. This field not only opens new possibilities for artistic creation but also provides powerful tools for generating personalized digital content, especially in the thriving era of AIGC. The complexity and diversity of Chinese characters pose significant challenges for font style transfer, prompting numerous solutions proposed by the research community. In light of these rapid advancements, we aim to provide a comprehensive review of the latest developments in Chinese font style transfer.Specifically, we first outline traditional methods, particularly those that existed before the emergence of deep learning techniques, to establish a theoretical foundation. Subsequently, we delve into the current mainstream deep learning methods, including Autoencoder, Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), and their applications in Chinese font style transfer. While these methods have shown remarkable performance in handling the shapes, structures, and artistic styles of Chinese characters, they still face inherent challenges and limitations. Therefore, we propose an innovative set of solutions aimed at overcoming these obstacles and improving conversion effectiveness and practicality. This is the first comprehensive study on font style transfer methods based on deep learning technology. We hope to provide new ideas and directions for future research and application of Chinese font style transfer. The undertaking of this work enriches theoretical research on style transfer and offers substantial support for practical applications, possessing profound significance and widespread impact.