Handwritten Chinese Font Generation with Collaborative Stroke Refinement
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021(2019)
摘要
Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters. This task has two main pain points: (i) handwritten characters are usually associated with thin strokes of few information and complex structure which are error prone during deformation; (ii) thousands of characters with various shapes are needed to synthesize based on a few manually designed characters. To solve those issues, we propose a novel convolutional-neural-network-based model with three main techniques: collaborative stroke refinement, using collaborative training strategy to recover the missing or broken strokes; online zoom-augmentation, taking the advantage of the content-reuse phenomenon to reduce the size of training set; and adaptive pre-deformation, standardizing and aligning the characters. The proposed model needs only 750 paired training samples; no pre-trained network, extra dataset resource or labels is needed. Experimental results show that the proposed method significantly outperforms the state-of-the-art methods under the practical restriction on handwritten font synthesis.
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关键词
handwritten Chinese font generation,collaborative stroke refinement,automatic character generation,appealing solution,typeface design,Chinese fonts,handwritten characters,error-prone,auxiliary branch,bold version,target characters,dominating branch,collaborative fashion,character synthesis model,manually designed characters,Chinese characters,online zoom-augmentation strategy,size training sets,handwritten font synthesis,practical setting,750 paired training samples
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