Title: Joint feature extraction technique for text detection from natural scene image

Authors: Ramgopal Segu; K. Suresh

Addresses: Department of Electronics and Communication Engineering, Dayanand Sagar College of Engineering, Bengaluru, India ' Department of Electronics and Communication Engineering, Sri Darmasthala Manjunatheswara Institute of Technology, Ujire, India

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 state-of-art text detection model.

Keywords: connected components; natural scene; shape analysis; SIFT analysis; text detection.

DOI: 10.1504/IJSISE.2017.084565

International Journal of Signal and Imaging Systems Engineering, 2017 Vol.10 No.1/2, pp.14 - 21

Received: 31 Oct 2016
Accepted: 24 Jan 2017
Published online: 14 Jun 2017 *

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