W-Net: One-Shot Arbitrary-Style Chinese Character Generation with Deep Neural Networks
This addresses the challenge of style-consistent Chinese character generation for applications like font design and handwriting synthesis, representing a novel advancement in the field.
The paper tackles the problem of generating Chinese characters with diverse styles from a single example, introducing the W-Net framework that learns and generates arbitrary characters sharing a similar style, with experimental results showing significant superiority in one-shot settings.
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.