Name
Abstract | ||
|---|---|---|
Personal handwriting style fonts generation is a diverting but time-consuming task due to the large size of Chinese character set. In addition, unlike standard printed style fonts, hand-writing style fonts are of more complicated stroke and glyph feature. In this paper, an improved network architecture is proposed for learning and generation of personal hand-writing style fonts based on small character set. The network is composed of three sub-networks: 1) a classification network for identifying the general style of the target fonts; 2) a generating network for transferring the identified fonts to the target fonts; 3) a discriminating network for differentiating the generated image from real ones. The experiments revealed the effectiveness of the model for generating personal hand-writing style font with relatively small data size, reduction by a scale of 10 comparing to previous reported works. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ROBIO.2018.8665297 | 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO) |
Field | DocType | Citations |
Glyph,Data modeling,Small data,Task analysis,Handwriting,Font,Network architecture,Control engineering,Artificial intelligence,Natural language processing,Engineering,Character encoding | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yutian Lei | 1 | 4 | 2.08 |
| Liguang Zhou | 2 | 2 | 3.07 |
| Tianjiao Pan | 3 | 0 | 0.34 |
| Huihuan Qian | 4 | 140 | 34.90 |
| zhenglong sun | 5 | 6 | 4.13 |