Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: T-HOG: An effective gradient-based descriptor for single line text regions
Author: Minetto, R
Thome, N
Cord, M
Leite, NJ
Stolfi, J
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.
Subject: Text detection
Text classification
Histogram of oriented gradients for text
Text descriptor
Country: Inglaterra
Editor: Elsevier Sci Ltd
Citation: Pattern Recognition. Elsevier Sci Ltd, v. 46, n. 3, n. 1078, n. 1090, 2013.
Rights: fechado
Identifier DOI: 10.1016/j.patcog.2012.10.009
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.