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Joint feature extraction technique for text detection from natural scene image

Joint feature extraction technique for text detection from natural scene image Detection text detection and extraction from natural scenes (i.e. video or images) can deliver significant information for various applications. To address the issue of text detection, a novel approach for text detection from natural scene image is introduced by developing a joint feature extraction method by considering shape and scale invariant feature transform (SIFT) feature analysis techniques. Shape extraction is improved by applying curvature-based shape analysis model. To construct the feature descriptor, input image is passed through canny edge detection process in which gradients are computed of each image. Later, we perform SIFT analysis and SIFT-based feature matching to formulate the SIFT feature descriptor. Finally, these two descriptors are merged together, and a combined descriptor is presented for text detection. Experimental study is carried out by considering benchmark ICDAR 2003, 2013 and 2015 data sets. Experimental study shows that proposed approach outperforms when compared with stateof-art text detection model. Keywords: connected components; natural scene; shape analysis; SIFT analysis; text detection. Reference to this paper should be made as follows: Segu, R. and Suresh, K. (2017) `Joint feature extraction technique for text detection from natural scene image', Int. J. Signal and Imaging Systems Engineering, Vol. 10, Nos. 1/2, pp.14­21. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Joint feature extraction technique for text detection from natural scene image

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Publisher
Inderscience Publishers
Copyright
Copyright © 2017 Inderscience Enterprises Ltd.
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2017.084565
Publisher site
See Article on Publisher Site

Abstract

Detection text detection and extraction from natural scenes (i.e. video or images) can deliver significant information for various applications. To address the issue of text detection, a novel approach for text detection from natural scene image is introduced by developing a joint feature extraction method by considering shape and scale invariant feature transform (SIFT) feature analysis techniques. Shape extraction is improved by applying curvature-based shape analysis model. To construct the feature descriptor, input image is passed through canny edge detection process in which gradients are computed of each image. Later, we perform SIFT analysis and SIFT-based feature matching to formulate the SIFT feature descriptor. Finally, these two descriptors are merged together, and a combined descriptor is presented for text detection. Experimental study is carried out by considering benchmark ICDAR 2003, 2013 and 2015 data sets. Experimental study shows that proposed approach outperforms when compared with stateof-art text detection model. Keywords: connected components; natural scene; shape analysis; SIFT analysis; text detection. Reference to this paper should be made as follows: Segu, R. and Suresh, K. (2017) `Joint feature extraction technique for text detection from natural scene image', Int. J. Signal and Imaging Systems Engineering, Vol. 10, Nos. 1/2, pp.14­21.

Journal

International Journal of Signal and Imaging Systems Engineering – Inderscience Publishers

Published: Jan 1, 2017

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