TY - GEN
T1 - Neural Font Style Transfer
AU - Atarsaikhan, Gantugs
AU - Iwana, Brian Kenji
AU - Narusawa, Atsushi
AU - Yanai, Keiji
AU - Uchida, Seiichi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/25
Y1 - 2018/1/25
N2 - In this paper, we chose an approach to generate fonts by using neural style transfer. Neural style transfer uses Convolution Neural Networks(CNN) to transfer the style of one image to another. By modifying neural style transfer, we can achieve neural font style transfer. We also demonstrate the effects of using different weighted factors, character placements, and orientations. In addition, we show the results of using non-Latin alphabets, non-text patterns, and non-text images as style images. Finally, we provide insight into the characteristics of style transfer with fonts.
AB - In this paper, we chose an approach to generate fonts by using neural style transfer. Neural style transfer uses Convolution Neural Networks(CNN) to transfer the style of one image to another. By modifying neural style transfer, we can achieve neural font style transfer. We also demonstrate the effects of using different weighted factors, character placements, and orientations. In addition, we show the results of using non-Latin alphabets, non-text patterns, and non-text images as style images. Finally, we provide insight into the characteristics of style transfer with fonts.
UR - http://www.scopus.com/inward/record.url?scp=85045266858&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045266858&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2017.328
DO - 10.1109/ICDAR.2017.328
M3 - Conference contribution
AN - SCOPUS:85045266858
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 51
EP - 56
BT - Proceedings - 1st Workshop of Machine Learning under International Conference on Document Analysis and Recognition, ICDAR-WML 2017
PB - IEEE Computer Society
T2 - 1st Workshop of Machine Learning under International Conference on Document Analysis and Recognition, ICDAR-WML 2017
Y2 - 11 November 2017
ER -