Text Localization and Character Extraction in Natural Scene Images using Contourlet Transform and SVM Classifier

Text Localization and Character Extraction in Natural Scene Images using Contourlet Transform and SVM Classifier

Автор: Shivananda V. Seeri, J. D. Pujari, P. S. Hiremath

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 5 vol.8, 2016 года.

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The objective of this study is to propose a new method for text region localization and character extraction in natural scene images with complex background. In this paper, a hybrid methodology is suggested which extracts multilingual text from natural scene image with cluttered backgrounds. The proposed approach involves four steps. First, potential text regions in an image are extracted based on edge features using Contourlet transform. In the second step, potential text regions are tested for text content or non-text using GLCM features and SVM classifier. In the third step, detection of multiple lines in localized text regions is done and line segmentation is performed using horizontal profiles. In the last step, each character of the segmented line is extracted using vertical profiles. The experimentation has been done using images drawn from own dataset and ICDAR dataset. The performance is measured in terms of the precision and recall. The results demonstrate the effectiveness of the proposed method, which can be used as an efficient method for text recognition in natural scene images.

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Natural scene images, Text localization, Contourlet transform, SVM classifier, GLCM, horizontal profile, vertical profile

Короткий адрес: https://sciup.org/15013974

IDR: 15013974

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