  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.
