  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.
