  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 strokes, 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.
