论文标题
通过样式和内容删除汉字的内容的多形字体到框的翻译
Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character
论文作者
论文摘要
本文主要讨论个性化字体作为图像样式转移的问题。本文的主要目的是设计一个可以提取和重组角色的内容和样式的网络框架。这些尝试可用于将整个字体综合使用少量字符。该论文结合了各种深度网络，例如卷积神经网络，多层感知器和残留网络，以找到最佳模型来提取字体字符的特征。结果表明，使用结构相似性索引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。
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.
