T-HOG: An effective gradient-based descriptor for single line text regions

  • Authors:

  • Rodrigo Minetto;Nicolas Thome;Matthieu Cord;Neucimar J. Leite;Jorge Stolfi

  • Affiliations:

  • DAINF, Federal University of Technology of Paraná, Curitiba, Brazil;Laboratoire d'Informatique Paris 6 (LIP6), Université Pierre et Marie Curie, Paris, France;Laboratoire d'Informatique Paris 6 (LIP6), Université Pierre et Marie Curie, Paris, France;Institute of Computing, University of Campinas, Campinas, Brazil;Institute of Computing, University of Campinas, Campinas, Brazil

  • Venue:
  • Pattern Recognition
  • Year:
  • 2013

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Abstract

We discuss the use of histogram of oriented gradients (HOG) descriptors as an effective tool for text description and recognition. Specifically, we propose a HOG-based texture descriptor (T-HOG) that uses a partition of the image into overlapping horizontal cells with gradual boundaries, to characterize single-line texts in outdoor scenes. The input of our algorithm is a rectangular image presumed to contain a single line of text in Roman-like characters. The output is a relatively short descriptor that provides an effective input to an SVM classifier. Extensive experiments show that the T-HOG is more accurate than Dalal and Triggs's original HOG-based classifier, for any descriptor size. In addition, we show that the T-HOG is an effective tool for text/non-text discrimination and can be used in various text detection applications. In particular, combining T-HOG with a permissive bottom-up text detector is shown to outperform state-of-the-art text detection systems in two major publicly available databases.