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Handwritten Chinese Font Generation with Collaborative Stroke Refinement

机译:手写的中国字体一代与协同卒中细化

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摘要

Automatic character generation is an appealing solution for typeface design, especially for Chinese fonts with over 3700 most commonly-used characters. This task is particularly challenging for handwritten characters with thin strokes which are error-prone during deformation. To handle the generation of thin strokes, we introduce an auxiliary branch for stroke refinement. The auxiliary branch is trained to generate the bold version of target characters which are then fed to the dominating branch to guide the stroke refinement. The two branches are jointly trained in a collaborative fashion. In addition, for practical use, it is desirable to train the character synthesis model with a small set of manually designed characters. Taking advantage of content-reuse phenomenon in Chinese characters, we further propose an online zoom-augmentation strategy to reduce the dependency on large size training sets. The proposed model is trained end-to-end and can be added on top of any method for font synthesis. Experimental results on handwritten font synthesis have shown that the proposed method significantly outperforms the state-of-the-art methods under practical setting, i.e. with only 750 paired training samples.
机译:自动字符生成是字体设计的一种吸引人的解决方案,尤其适用于具有超过3700多个最常用的字符的中文字体。这项任务尤其具有挑战性,手写字符具有薄笔划,在变形期间出错。为了处理薄型的生成,我们向中风细化引入辅助分支。辅助分支训练以生成目标字符的粗体版本,然后将其馈送到主导分支以指导笔划细化。这两个分支是以合作方式培训的。此外,对于实际使用,希望用一小部分手动设计的字符训练字符综合模型。利用汉字中的内容重用现象,我们进一步提出了在线缩放增强策略,以减少对大型训练集的依赖。所提出的模型训练结束到底,可以在任何用于字体合成的方法之上添加。手写字体合成的实验结果表明,该方法在实际设置下显着优于最先进的方法,即仅具有750个配对训练样本。

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