T-HOG
T-HOG: an Effective Gradient-Based Descriptor for Single Line Text Regions
Pattern Recognition (PR), Elsevier, vol. 46, pp. 1078-1090, 2013.
Rodrigo Minetto , Nicolas Thome , Matthieu Cord , Neucimar J. Leite , Jorge Stolfi
Federal University of Technology of Parana, DAINF, Curitiba, Brazil
Universite Pierre et Marie Curie, UPMC-Sorbonne Universities, LIP6, Paris, France
University of Campinas, Institute of Computing, Campinas, Brazil
We discuss the use of the 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 and video frames. The input of
our algorithm is a rectangular image presumed to contain a single line of text
in Latin-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 recognizer is more accurate than Dalal and Triggs's original
HOG-based classifier, for any descriptor size. In addition, we 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, as described in our journal
[thog-paper]. Read also our text detector paper: [text detection].