Name
Abstract | ||
|---|---|---|
•Improved method that achieves state of the art performance in image-based text retrieval.•Results on different CNN backbones modified to predict a PHOC of detected textual instances are presented.•Effect of different PHOC dimensions is explored and analyzed.•PHOC embedding allows retrieving out-of-vocabulary words unseen at training time.•Proposed method achieves state of the art in multilingual dataset of unseen samples at training time.•Method is faster than state of the art, allowing real-time retrieval in videos. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107656 | Pattern Recognition |
Keywords | DocType | Volume |
Image retrieval,Scene text detection,Scene text recognition,Word spotting,Convolutional neural networks,Region proposal networks,PHOC | Journal | 110 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Mafla | 1 | 12 | 2.89 |
| Ruben Tito | 2 | 10 | 2.18 |
| Sounak Dey | 3 | 0 | 0.34 |
| Lluís Gómez | 4 | 93 | 8.74 |
| Marçal Rusiñol | 5 | 386 | 33.57 |
| E. Valveny | 6 | 196 | 11.82 |
| Dimosthenis Karatzas | 7 | 406 | 38.13 |