Local Style Awareness of Font Images
Daichi Haraguchi Seiichi Uchida
When we compare fonts, we often pay attention to styles of local parts, such
as serifs and curvatures. This paper proposes an attention mechanism to find
important local parts. The local parts with larger attention are then
considered important. The proposed mechanism can be trained in a
quasi-self-supervised manner that requires no manual annotation other than
knowing that a set of character images is from the same font, such as
Helvetica. After confirming that the trained attention mechanism can find
style-relevant local parts, we utilize the resulting attention for local
style-aware font generation. Specifically, we design a new reconstruction loss
function to put more weight on the local parts with larger attention for
generating character images with more accurate style realization. This loss
function has the merit of applicability to various font generation models. Our
experimental results show that the proposed loss function improves the quality
of generated character images by several few-shot font generation models.