A Skeleton Based Descriptor for Detecting Text in Real Scene Images

Mehdi Felhi 1, 2 Nicolas Bonnier 2 Salvatore Tabbone 1
1 QGAR - Querying Graphics through Analysis and Recognition
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper, we present a new method for text extraction in real scene images. We propose first a skeleton based descriptor to describe the strokes of the text candidates that compose a spatial relation graph. We then apply the graph cuts algorithm to label the nodes of the graph as text or non-text. We finally refine the resulted text lines candidates by classifying them using a kernel SVM. To validate this approach we perform a set of tests on the public datasets ICDAR 2003 and 2011.
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Communication dans un congrès
21st International Conference on Pattern Recognition - ICPR 2012, Nov 2012, Tsukuba, Japan. IEEE, pp.282-285, 2012, 〈http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460127〉
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Mehdi Felhi, Nicolas Bonnier, Salvatore Tabbone. A Skeleton Based Descriptor for Detecting Text in Real Scene Images. 21st International Conference on Pattern Recognition - ICPR 2012, Nov 2012, Tsukuba, Japan. IEEE, pp.282-285, 2012, 〈http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460127〉. 〈hal-00764633〉

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