Chinese Typography Transfer [article]

Jie Chang, Yujun Gu
<span title="2017-08-02">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a new network architecture for Chinese typography transformation based on deep learning. The architecture consists of two sub-networks: (1)a fully convolutional network(FCN) aiming at transferring specified typography style to another in condition of preserving structure information; (2)an adversarial network aiming at generating more realistic strokes in some details. Unlike models proposed before 2012 relying on the complex segmentation of Chinese components or
more &raquo; ... s, our model treats every Chinese character as an inseparable image, so pre-processing or post-preprocessing are abandoned. Besides, our model adopts end-to-end training without pre-trained used in other deep models. The experiments demonstrates that our model can synthesize realistic-looking target typography from any source typography both on printed style and handwriting style.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1707.04904v2</a> <a target="_blank" rel="external noopener" href="">fatcat:vd7s45nzmzbkvf6jic2d224qmq</a> </span>
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