  Different fonts have different impressions, such as elegant, scary, and cool.
This paper tackles part-based shape-impression analysis based on the
Transformer architecture, which is able to handle the correlation among local
parts by its self-attention mechanism. This ability will reveal how
combinations of local parts realize a specific impression of a font. The
versatility of Transformer allows us to realize two very different approaches
for the analysis, i.e., multi-label classification and translation. A
quantitative evaluation shows that our Transformer-based approaches estimate
the font impressions from a set of local parts more accurately than other
approaches. A qualitative evaluation then indicates the important local parts
for a specific impression.
