Cohen's Class Distributions for Skew Angle Estimation in Noisy Ancient Arabic Documents

Nazih Ouwayed 1 Abdel Belaïd 1 François Auger 2
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LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This paper presents an adaptive method for the skew angle estimation of noisy handwritten Arabic documents using the energy distributions of Cohen's class. The presence of noise in raw document images can create local maxima which disturb the projection histogram analysis. To avoid this drawback, we propose to use Cohen's class distributions on the projection histogram profiles obtained using different projection angles. These distributions reduce the importance of these local maxima and are fitted to the nonstationary nature of these histogram profiles. The orientation of the document is given by the highest maximum. The proposed skew angle detection technique has been evaluated on 864 skewed documents. The results of each distribution are presented at the end of this paper.
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Communication dans un congrès
Daniel Lopresti and Shourya Roy and Klaus Schulz and L. Venkata Subramaniam,. Third Workshop on Analytics for Noisy Unstructured Text Data - AND 2009, Jul 2009, Barcelona, Spain. ACM, pp.41-46, 2009, ACM International Conference Series. 〈http://portal.acm.org/toc.cfm?id=1568296〉. 〈10.1145/1568296.1568305〉
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Soumis le : mardi 24 novembre 2009 - 08:46:21
Dernière modification le : mardi 24 avril 2018 - 13:37:56
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Nazih Ouwayed, Abdel Belaïd, François Auger. Cohen's Class Distributions for Skew Angle Estimation in Noisy Ancient Arabic Documents. Daniel Lopresti and Shourya Roy and Klaus Schulz and L. Venkata Subramaniam,. Third Workshop on Analytics for Noisy Unstructured Text Data - AND 2009, Jul 2009, Barcelona, Spain. ACM, pp.41-46, 2009, ACM International Conference Series. 〈http://portal.acm.org/toc.cfm?id=1568296〉. 〈10.1145/1568296.1568305〉. 〈inria-00435253〉

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