T-HOG: an Effective Gradient-Based Descriptor for Single Line Text Regions

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.
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Rodrigo Minetto, Nicolas Thome, Matthieu Cord, Neucimar J. Leite, Jorge Stolfi. T-HOG: an Effective Gradient-Based Descriptor for Single Line Text Regions. Pattern Recognition, Elsevier, 2013, 46 (3), pp.1078-1090. 〈10.1016/j.patcog.2012.10.009〉. 〈hal-01185468〉



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