Font Style Conversion Based on Deep Learning

DOI

10.2991/ncce-18.2018.155How to use a DOI?

Keywords

Deep learning; style conversion; generative adversarial network; structure generated clear.

Abstract

In view of the cost of traditional design of new fonts, a method of combining deep learning for font style conversion is proposed. By using a U-Net type network structure combining the training method of generative adversarial network, supervised learning part of fonts, the font style conversion ability of the font generator network is constantly enhanced so that the fonts can be converted to another style through the generator network. The experimental results show that this method convert font structure generated clear and smooth, less noise, and the original character of the same font highly consistent in size, weight, style etc.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Da Lv
AU  - Yijun Liu
PY  - 2018/05
DA  - 2018/05
TI  - Font Style Conversion Based on Deep Learning
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 922
EP  - 926
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.155
DO  - 10.2991/ncce-18.2018.155
ID  - Lv2018/05
ER  -