TY - GEN
T1 - Neural style difference transfer and its application to font generation
AU - Atarsaikhan, Gantugs
AU - Iwana, Brian Kenji
AU - Uchida, Seiichi
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP17H06100.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Designing fonts requires a great deal of time and effort. It requires professional skills, such as sketching, vectorizing, and image editing. Additionally, each letter has to be designed individually. In this paper, we introduce a method to create fonts automatically. In our proposed method, the difference of font styles between two different fonts is transferred to another font using neural style transfer. Neural style transfer is a method of stylizing the contents of an image with the styles of another image. We proposed a novel neural style difference and content difference loss for the neural style transfer. With these losses, new fonts can be generated by adding or removing font styles from a font. We provided experimental results with various combinations of input fonts and discussed limitations and future development for the proposed method.
AB - Designing fonts requires a great deal of time and effort. It requires professional skills, such as sketching, vectorizing, and image editing. Additionally, each letter has to be designed individually. In this paper, we introduce a method to create fonts automatically. In our proposed method, the difference of font styles between two different fonts is transferred to another font using neural style transfer. Neural style transfer is a method of stylizing the contents of an image with the styles of another image. We proposed a novel neural style difference and content difference loss for the neural style transfer. With these losses, new fonts can be generated by adding or removing font styles from a font. We provided experimental results with various combinations of input fonts and discussed limitations and future development for the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85090099236&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-57058-3_38
DO - 10.1007/978-3-030-57058-3_38
M3 - Conference contribution
AN - SCOPUS:85090099236
SN - 9783030570576
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 544
EP - 558
BT - Document Analysis Systems - 14th IAPR International Workshop, DAS 2020, Proceedings
A2 - Bai, Xiang
A2 - Karatzas, Dimosthenis
A2 - Lopresti, Daniel
PB - Springer
T2 - 14th IAPR International Workshop on Document Analysis Systems, DAS 2020
Y2 - 26 July 2020 through 29 July 2020
ER -