Abstract
Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an efficient and generalized deep framework, namely, the W-Net, is introduced for the one-shot arbitrary-style Chinese character generation task. Specifically, given a single character (one-shot) with a specific style (e.g., a printed font or hand-writing style), the proposed W-Net model is capable of learning and generating any arbitrary characters sharing the style similar to the given single character. Such appealing property was rarely seen in the literature. We have compared the proposed W-Net framework to many other competitive methods. Experimental results showed the proposed method is significantly superior in the one-shot setting.
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Acknowledgements
The work was partially supported by the following: National Natural Science Foundation of China under no. 61473236 and 61876155; Natural Science Fund for Colleges and Universities in Jiangsu Province under no. 17KJD520010; Suzhou Science and Technology Program under no. SYG201712, SZS201613; Jiangsu University Natural Science Research Programme under grant no. 17KJB-520041; Key Program Special Fund in XJTLU under no. KSF-A-01 and KSF-P-02.
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Jiang, H., Yang, G., Huang, K., Zhang, R. (2018). W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_43
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