IEEE, Venice, Italy (2017)Ĭhoi, Y., Choi, M., Kim, M., et al.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. Proceedings of the IEEE international conference on computer vision, pp. Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks. IEEE, Las Vegas (2016)Ĭhen, D., Yuan, L., Liao, J., et al.: StyleBank: an explicit representation for neural image style transfer.

Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. Li, Y.N., Wang, P., Xiao, J.L.: An image style transfer algorithm using multi-dimensional histogram matching. Ultimately the resulting character style is basically close to the manually designed one, which reaches the target of the font style transfer.

Experiments are conducted based on the Chinese character datasets. To train the network, we exploit the root mean square optimizer to automatically adjust the deep learning rate and gradually reduce the difference values. In this paper, a convolutional neural network model is proposed to be applied into the Chinese character style migration. Therefore, it is necessary to design a model that can automatically generate new Chinese characters in a specified style. It usually relies on traditional manual methods or computer-aided design for each Chinese character, which is time-consuming and labor-intensive. Designing a set of new fonts with a specific style is a very tedious and massive task. With various styles, Chinese characters are one of the most important cultural symbols in China.

Chinese font style list