Generating new fonts is a time-consuming and labor-intensive, especially in a language with a huge amount of characters like Chinese. Various deep learning models have demonstrated the ability to efficiently generate new fonts with a few reference characters of that style. This project aims to develop a few-shot cross-lingual font generator based on AGIS-Net and improve the performance metrics mentioned. Our approaches include redesigning the encoder and the loss function. We will validate our method on multiple languages and datasets mentioned.
翻译:生成新字体既费时又费力, 特别是使用像中文这样的有大量字符的语言。 各种深层学习模式已经证明能够高效生成带有该样式的几个参考字符的新字体。 该项目旨在开发一个基于 AGIS- Net 的、 几发跨语言的跨语言字体生成器, 并改进上述的性能度量度。 我们的方法包括重新设计编码器和丢失功能。 我们将验证我们提到的多种语言和数据集的方法 。