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Proceedings Paper

Off-lexicon online Arabic handwriting recognition using neural network
Author(s): Hamdi Yahia; Aymen Chaabouni; Houcine Boubaker; Adel M. Alimi
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Paper Abstract

This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410G (17 March 2017); doi: 10.1117/12.2268650
Show Author Affiliations
Hamdi Yahia, Univ. de Sfax (Tunisia)
Aymen Chaabouni, Univ. de Sfax (Tunisia)
Houcine Boubaker, Univ. de Sfax (Tunisia)
Adel M. Alimi, Univ. de Sfax (Tunisia)

Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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