Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not yet been integrated with deep learning architectures in a manner that supports their multi-scale nature. In this work we aim to bridge this gap, proposing a network architecture capable of rasterizing glyphs in multiple sizes, potentially paving the way for easy and accessible creation and manipulation of fonts.

翻译：最近深层学习技术和应用的进步使艺术创造和操纵在许多领域(文字、图像、音乐)发生了革命性的变化;然而,字体还没有以支持其多尺度性质的方式与深层学习结构融合在一起。 在这项工作中,我们的目标是弥合这一差距,提出一个能够将多种大小的晶体加以分解的网络结构,为方便和方便地创建和操纵字体铺平道路。