Name
Abstract | ||
|---|---|---|
This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00134 | ICDAR |
Field | DocType | Citations |
Transcription (linguistics),Computer vision,Computer science,Pixel,Artificial intelligence,Detector,Text detection | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raul Gomez | 1 | 5 | 2.12 |
| Ali Furkan Biten | 2 | 9 | 2.18 |
| Lluís Gómez | 3 | 0 | 0.34 |
| Jaume Gibert | 4 | 79 | 5.99 |
| Dimosthenis Karatzas | 5 | 406 | 38.13 |
| Marçal Rusiñol | 6 | 386 | 33.57 |