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|Type:||Artigo de periódico|
|Title:||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 Dalai 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. (C) 2012 Elsevier Ltd. All rights reserved.|
Histogram of oriented gradients for text
|Editor:||Elsevier Sci Ltd|
|Citation:||Pattern Recognition. Elsevier Sci Ltd, v. 46, n. 3, n. 1078, n. 1090, 2013.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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