  Generating a new font library is a very labor-intensive and time-consuming
job for glyph-rich scripts. Despite the remarkable success of existing font
generation methods, they have significant drawbacks; they require a large
number of reference images to generate a new font set, or they fail to capture
detailed styles with only a few samples. In this paper, we focus on
compositional scripts, a widely used letter system in the world, where each
glyph can be decomposed by several components. By utilizing the
compositionality of compositional scripts, we propose a novel font generation
framework, named Dual Memory-augmented Font Generation Network (DM-Font), which
enables us to generate a high-quality font library with only a few samples. We
employ memory components and global-context awareness in the generator to take
advantage of the compositionality. In the experiments on Korean-handwriting
fonts and Thai-printing fonts, we observe that our method generates a
significantly better quality of samples with faithful stylization compared to
the state-of-the-art generation methods quantitatively and qualitatively.
Source code is available at https://github.com/clovaai/dmfont.
