HandwritingAgent generates SVG handwriting strokes via a large reasoning model conditioned on text and a style reference image, matching or exceeding prior generative models on imitation, recognition, multilingual, and math expression tasks without style-specific training.
Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis
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abstract
In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images (Fig. 1(a)). Recent works mostly follow the stroke extraction and assemble pipeline which is complex in the process and limited by the effect of stroke extraction. We treat the calligraphy synthesis problem as an image-to-image translation problem and propose a deep neural network based model which can generate calligraphy images from standard font images directly. Besides, we also construct a large scale benchmark that contains various styles for Chinese calligraphy synthesis. We evaluate our method as well as some baseline methods on the proposed dataset, and the experimental results demonstrate the effectiveness of our proposed model.
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HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space
HandwritingAgent generates SVG handwriting strokes via a large reasoning model conditioned on text and a style reference image, matching or exceeding prior generative models on imitation, recognition, multilingual, and math expression tasks without style-specific training.