Abstract
In this paper, we study about font generation and conversion. The previous methods dealt with characters as ones made of strokes. On the contrary, we extract features, which are equivalent to the strokes, from font images and texture or pattern images using deep learning, and transform the design pattern of font images. We expect that generation of original font such as hand written characters will be generated automatically by the proposed approach. In the experiments, we have created unique datasets such as a ketchup character image dataset and improve image generation quality and readability of character by combining neural style transfer with unsupervised cross-domain learning.
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References
Ulyanov, D., Lebedev, V., Lempitsky, A.V.: Texture networks: feed-forward synthesis of textures and stylized images. In: arXiv:1603.03417v1 (2016)
Gantugs, A., Iwana, B.K., Narusawa, A., Yanai, K., Uchida, S.: Neural font style transfer. In: International Conference on Document Analysis and Recognition (2017)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of IEEE Computer Vision and Pattern Recognition (2016)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of European Conference on Computer Vision (2016)
Jun-Yan, Z., Taesung, P., Phillip, I., Alexei, E.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of IEEE International Conference on Computer Vision (2017)
Kim, T., Cha, M., Kim, M., Lee, J.K., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. In: International Conference on Machine Learning (2017). http://arxiv.org/abs/1703.05192
Li, C., Wand, M.: Combining Markov random fields and convolutional neural networks for image synthesis. In: Proceedings of IEEE Computer Vision and Pattern Recognition (2016)
Lin, J., Hong, C., Chang, R., Wang, Y., Lin, S., Ho, J.: Complete font generation of Chinese characters in personal handwriting style. In: 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC), pp. 1–5. IEEE (2015)
Miyazaki, T., et al.: Automatic generation of typographic font from a small font subset. In: arXiv preprint arXiv:1701.05703 (2017)
Phillip, I., Jun-Yan, Z., Tinghui, Z., Alexei, E.: Image-to-image translation with conditional adversarial networks. In: Proceedings of IEEE Computer Vision and Pattern Recognition (2017)
Shuai, Y., Jiaying, L., Zhouhui, L., Zongming, G.: Awesome typography: statistics-based text effects transfer. In: Proceedings of IEEE Computer Vision and Pattern Recognition (2016)
Songhua, X., Lau, F.C.M., Kwok-Wai, C., Yunhe, P.: Automatic generation of artistic Chinese calligraphy. In: IEEE Intelligent Systems (2005)
Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. In: International Conference on Learning Representation (2017)
Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: unsupervised dual learning for image-to-image translation. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2849–2857 (2017)
Zong, A., Zhu, Y.: StrokeBank: automating personalized Chinese handwriting generation. In: Advancement of Artificial Intelligence, pp. 3024–3030 (2014)
Acknowledgments
We would like to express great thanks to Prof. Seichi Uchida, Kyushu University, for the insightful and helpful comments. This work was supported by JSPS KAKENHI Grant Number 15H05915, 17H01745, 17H05972, 17H06026 and 17H06100.
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Narusawa, A., Shimoda, W., Yanai, K. (2019). Font Style Transfer Using Neural Style Transfer and Unsupervised Cross-domain Transfer. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_9
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