TY - GEN
T1 - Impressions2Font
T2 - 16th International Conference on Document Analysis and Recognition, ICDAR 2021
AU - Matsuda, Seiya
AU - Kimura, Akisato
AU - Uchida, Seiichi
N1 - Funding Information:
supported by JSPS KAKENHI Grant Number
Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP17H06100.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-030-86334-0_48
DO - 10.1007/978-3-030-86334-0_48
M3 - Conference contribution
AN - SCOPUS:85115303006
SN - 9783030863333
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 739
EP - 754
BT - Document Analysis and Recognition - ICDAR 2021 - 16th International Conference, Proceedings
A2 - Lladós, Josep
A2 - Lopresti, Daniel
A2 - Uchida, Seiichi
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 5 September 2021 through 10 September 2021
ER -