论文标题
深度度量学习几乎没有弹射字体
Few-Shot Font Generation with Deep Metric Learning
论文作者
论文摘要
为具有大量字符的语言设计字体，例如日语和中文，是一项非常耗时且耗时的任务。在这项研究中，我们解决了仅从几个字体样品中自动生成日本印刷字体的问题，其中合成的字形有望具有连贯的特性，例如骨骼，轮廓和串行。当样式参考字形的数量极为有限时，现有方法通常无法生成精细的字形图像。本文中，我们提出了一个简单但功能强大的框架，用于提取更好的样式功能。该框架将深度度量学习引入了样式编码器。我们使用黑白和形状差的字体数据集进行了实验，并证明了所提出的框架的有效性。
Designing fonts for languages with a large number of characters, such as Japanese and Chinese, is an extremely labor-intensive and time-consuming task. In this study, we addressed the problem of automatically generating Japanese typographic fonts from only a few font samples, where the synthesized glyphs are expected to have coherent characteristics, such as skeletons, contours, and serifs. Existing methods often fail to generate fine glyph images when the number of style reference glyphs is extremely limited. Herein, we proposed a simple but powerful framework for extracting better style features. This framework introduces deep metric learning to style encoders. We performed experiments using black-and-white and shape-distinctive font datasets and demonstrated the effectiveness of the proposed framework.
