PT Journal
AU Dena Bazazian
   Raul Gomez
   Anguelos Nicolaou
   Lluis Gomez
   Dimosthenis Karatzas
   Andrew Bagdanov
TI Fast: Facilitated and accurate scene text proposals through fcn guided pruning
SO Pattern Recognition Letters
JI PRL
PY 2019
BP 112
EP 120
VL 119
AB Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition.
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