FAST: Facilitated and Accurate Scene Text Proposals through FCN Guided Pruning

Dena Bazazian, Raúl Gómez, Anguelos Nicolaou, Lluís Gómez, Dimosthenis Karatzas, Andrew D. Bagdanov

Research output: Contribution to journalArticleResearch

9 Citations (Scopus)


© 2017 Elsevier B.V. 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.
Original languageEnglish
Pages (from-to)112-120
JournalPattern Recognition Letters
Publication statusPublished - 1 Mar 2019


  • Fully convolutional networks
  • Scene text images
  • Text proposals


Dive into the research topics of 'FAST: Facilitated and Accurate Scene Text Proposals through FCN Guided Pruning'. Together they form a unique fingerprint.

Cite this