W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917689244516352 |
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| author | Jiang, Haochuan Yang, Guanyu Huang, Kaizhu Zhang, Rui |
| author_facet | Jiang, Haochuan Yang, Guanyu Huang, Kaizhu Zhang, Rui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks Jiang, Haochuan Yang, Guanyu Huang, Kaizhu Zhang, Rui Computer Vision and Pattern Recognition 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. |
| title | W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.06122 |