Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDAR.2017.328 | 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | Field | DocType |
neural style transfer,convolutional neural network,font generation | Pattern recognition,Convolution,Computer science,Font,Artificial intelligence,Artificial neural network | Conference |
Volume | ISSN | ISBN |
05 | 1520-5363 | 978-1-5386-3587-2 |
Citations | PageRank | References |
2 | 0.38 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gantugs Atarsaikhan | 1 | 2 | 0.72 |
| Brian Kenji Iwana | 2 | 7 | 6.58 |
| Atsushi Narusawa | 3 | 2 | 0.38 |
| Keiji Yanai | 4 | 908 | 98.05 |
| Seiichi Uchida | 5 | 790 | 105.59 |