  This paper mainly discusses the generation of personalized fonts as the
problem of image style transfer. The main purpose of this paper is to design a
network framework that can extract and recombine the content and style of the
characters. These attempts can be used to synthesize the entire set of fonts
with only a small amount of characters. The paper combines various depth
networks such as Convolutional Neural Network, Multi-layer Perceptron and
Residual Network to find the optimal model to extract the features of the fonts
character. The result shows that those characters we have generated is very
close to real characters, using Structural Similarity index and Peak
Signal-to-Noise Ratio evaluation criterions.
