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arXiv · 1904.13268

Handwritten Chinese Font Generation with Collaborative Stroke Refinement

Chuan Wen·Jie Chang·Ya Zhang·Siheng Chen·Yanfeng Wang·Mei Han·Qi Tian
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Abstract

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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Chuan Wen, Jie Chang, Ya Zhang, Siheng Chen, Yanfeng Wang, Mei Han, Qi Tian. 2019-04-30. Handwritten Chinese Font Generation with Collaborative Stroke Refinement. https://arxiv.org/abs/1904.13268

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