An efficient multiple classifier system for Arabic handwritten words recognition

doi:10.1016/j.patrec.2017.01.020

An efficient multiple classifier system for Arabic handwritten words recognition

2017-07-01

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DOI：10.1016/j.patrec.2017.01.020

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In this paper, we propose an efficient multiple classifier system for Arabic handwritten words recognition. First, we use Chebyshev moments (CM) enhanced with some Statistical and Contour-based Features (SCF) for describing word images. Then, we combine several classifiers integrated at the decision level. We consider the multilayer perceptron (MLP), the support vector machine (SVM) and the Extreme Learning Machine (ELM) classifiers. We propose several combination rules between MLP, SVM and ELM classifiers trained with CM and SCF features. Further, we consider a second level of combination that merges three best rules among the proposed ones. The system is evaluated on the IFN/ENIT database and compared to some well-known systems for Arabic handwriting recognition. The numerical results are competitive and show that our system is able to achieve a global recognition rate equal to 96.82% for the considered dataset. (C) 2017 Elsevier B.V. All rights reserved.

Keywords:

Arabic handwriting recognition

K-NN

MLP

SVM

ELM

Orthogonal moments (OM)

Chebyshev moments (CM)

Statistical and contour-based features (SCF)

An efficient multiple classifier system for Arabic handwritten words recognition

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