TY - GEN
T1 - Serif or sans
T2 - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
AU - Shinahara, Yuto
AU - Karamatsu, Takuro
AU - Harada, Daisuke
AU - Yamaguchi, Kota
AU - Uchida, Seiichi
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP17H06100.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - In this paper, we conduct a large-scale study of font statistics in book covers and online advertisements. Through the statistical study, we try to understand how graphic designers relate fonts and content genres and identify the relationship between font styles, colors, and genres. We propose an automatic approach to extract font information from graphic designs by applying a sequence of character detection, style classification, and clustering techniques to the graphic designs. The extracted font information is accumulated together with genre information, such as romance or business, for further trend analysis. Through our unique empirical study, we show that the collected font statistics reveal interesting trends in terms of how typographic design represents the impression and the atmosphere of the content genres.
AB - In this paper, we conduct a large-scale study of font statistics in book covers and online advertisements. Through the statistical study, we try to understand how graphic designers relate fonts and content genres and identify the relationship between font styles, colors, and genres. We propose an automatic approach to extract font information from graphic designs by applying a sequence of character detection, style classification, and clustering techniques to the graphic designs. The extracted font information is accumulated together with genre information, such as romance or business, for further trend analysis. Through our unique empirical study, we show that the collected font statistics reveal interesting trends in terms of how typographic design represents the impression and the atmosphere of the content genres.
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UR - http://www.scopus.com/inward/citedby.url?scp=85079869582&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2019.00170
DO - 10.1109/ICDAR.2019.00170
M3 - Conference contribution
AN - SCOPUS:85079869582
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1041
EP - 1046
BT - Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
PB - IEEE Computer Society
Y2 - 20 September 2019 through 25 September 2019
ER -