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About this paper
Weiran Chen, Guiqian Zhu, Ying Li, Yi Ji, Chunping Liu
Researchers at Soochow University's School of Computer Science and Technology provide a comprehensive survey of deep learning-based Chinese font generation from 2016 to 2025, introducing a novel taxonomy based on reference data availability (many-shot vs. few-shot). The work systematically reviews methodologies, identifies their strengths and limitations, and outlines future research directions in the field.
Key insights
- Deep learning has fundamentally transformed Chinese font generation by enabling sophisticated feature extraction and pattern synthesis that surpass traditional rule-based and morphology-based methods.
- The field is increasingly moving towards few-shot generation techniques, with structural-feature-based methods offering more precise control over intricate details compared to universal-feature-based approaches.
- The integration of advanced deep learning models like Transformers and Diffusion models marks a trend towards more data-efficient and higher-quality font synthesis, addressing challenges of fidelity and diversity.
Summary by alphaXiv.
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