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.

中文翻译：

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通过汉字的样式和内容解开表示的多形式字体到字体的翻译

本文主要讨论个性化字体的生成作为图像风格迁移的问题。本文的主要目的是设计一个可以提取和重组字符内容和风格的网络框架。这些尝试可用于仅用少量字符合成整个字体集。论文结合卷积神经网络、多层感知器和残差网络等各种深度网络，寻找最优模型来提取字体字符的特征。结果表明，使用结构相似性指数和峰值信噪比评估标准，我们生成的那些字符非常接近真实字符。