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

摘要

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.