With the development of deep learning technology, many deep learning methods have been applied to font recognition and generation. However, few studies focus on font inpainting problems. This paper is dedicated to repairing damaged fonts based on style to repair damaged fonts in a better way. In this paper, we propose a CGAN (Conditional Generative Adversarial Nets)-based font repair method. This paper uses the content accuracy and style similarity of the repaired image as an evaluation index to evaluate the accuracy of the restored style font. The font content proposed by the paper based on CGAN network repair style is similar with the correct content.

随着深度学习技术的发展，许多深度学习方法已经应用于字体识别和生成。但是，很少有研究关注字体修复问题。本文致力于根据样式修复损坏的字体，从而以更好的方式修复损坏的字体。在本文中，我们提出了一种基于CGAN（有条件生成对抗网络）的字体修复方法。本文以修复图像的内容准确性和风格相似度为评价指标，对修复后的风格字体的准确性进行评价。本文基于CGAN网络修复风格提出的字体内容与正确的内容相似。