  Various fonts give us various impressions, which are often represented by
words. This paper proposes Impressions2Font (Imp2Font) that generates font
images with specific impressions. Imp2Font is an extended version of
conditional generative adversarial networks (GANs). More precisely, Imp2Font
accepts an arbitrary number of impression words as the condition to generate
the font images. These impression words are converted into a soft-constraint
vector by an impression embedding module built on a word embedding technique.
Qualitative and quantitative evaluations prove that Imp2Font generates font
images with higher quality than comparative methods by providing multiple
impression words or even unlearned words.
