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

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.

► We discuss the use of the histogram of oriented gradients for text classification.
► We propose a novel HOG-based texture descriptor nicknamed T-HOG.
► The T-HOG allows to characterize single-line texts in outdoor scenes and video frames.
► We show that the T-HOG is an effective tool for text/non-text discrimination.
► We show that the T-HOG can be used in various text detection applications.