An unsupervised font style transfer model based on generative adversarial networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-021-11777-0.pdf
Reference53 articles.
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2. Azadi, S., Fisher, M., Kim, V. G., Wang, Z., Shechtman, E., & Darrell, T. (2018). Multi-content Gan for few-shot font style transfer. In proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7564-7573).
3. Baxter W, Govindaraju N (2010, February). Simple data-driven modeling of brushes. In proceedings of the 2010 ACM SIGGRAPH symposium on interactive 3D graphics and games (pp. 135-142).
4. Chang B, Zhang Q, Pan S, Meng L (2018, March) Generating handwritten chinese characters using cyclegan. In 2018 IEEE winter conference on applications of computer vision (WACV) (pp. 199-207). IEEE.
5. Chen J, Ji Y, Chen H, Xu X (2019) Learning one-to-many stylised Chinese character transformation and generation by generative adversarial networks. IET Image Process 13(14):2680–2686
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