Name
Abstract | ||
|---|---|---|
Text Proposals have emerged as a class-dependent version of object proposals - efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text recognition. In this paper we propose an improvement over the original Text Proposals algorithm of Gomez and Karatzas (2016), combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Pattern recognition,Ranking,Computer science,Artificial intelligence,Machine learning,Text recognition |
DocType | Volume | Citations |
Journal | abs/1702.05089 | 2 |
PageRank | References | Authors |
0.39 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dena Bazazian | 1 | 8 | 2.89 |
| Raul Gomez | 2 | 5 | 2.12 |
| Anguelos Nicolaou | 3 | 104 | 10.14 |
| Lluís Gómez i Bigorda | 4 | 2 | 0.73 |
| Dimosthenis Karatzas | 5 | 406 | 38.13 |
| Andrew D. Bagdanov | 6 | 861 | 52.78 |