Handwritten Chinese Font Generation with Collaborative Stroke Refinement
This work addresses the challenge of designing new Chinese typefaces efficiently, which is incremental as it builds on existing font synthesis methods.
The paper tackles the problem of generating handwritten Chinese fonts with limited training data by proposing a CNN-based model with collaborative stroke refinement, online zoom-augmentation, and adaptive pre-deformation, achieving significant outperformance over state-of-the-art methods under practical restrictions.
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.