Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-06555-2_1 | Document Analysis Systems |
Keywords | DocType | Volume |
Font shape, Impression analysis, Translator | Conference | 13237 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masaya Ueda | 1 | 0 | 0.34 |
| Akisato Kimura | 2 | 0 | 0.34 |
| Seiichi Uchida | 3 | 790 | 105.59 |