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• W4320007092 abstract "Previous works on font generation mainly focus on the standard print fonts where character's shape is stable and strokes are clearly separated. There is rare research on brush handwriting font generation, which involves holistic structure changes and complex strokes transfer. To address this issue, we propose a novel GAN-based image translation model by integrating the skeleton information. We first extract the skeleton from training images, then design an image encoder and a skeleton encoder to extract corresponding features. A self-attentive refined attention module is devised to guide the model to learn distinctive features between different domains. A skeleton discriminator is involved to first synthesize the skeleton image from the generated image with a pre-trained generator, then to judge its realness to the target one. We also contribute a large-scale brush handwriting font image dataset with six styles and 15,000 high-resolution images. Both quantitative and qualitative experimental results demonstrate the competitiveness of our proposed model." @default.
• W4320007092 created "2023-02-11" @default.
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• W4320007092 date "2022-04-21" @default.
• W4320007092 modified "2023-09-23" @default.
• W4320007092 title "SE-GAN: Skeleton Enhanced GAN-based Model for Brush Handwriting Font Generation" @default.
• W4320007092 doi "https://doi.org/10.48550/arxiv.2204.10484" @default.
• W4320007092 hasPublicationYear "2022" @default.
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