Multiclassifiers system for handwritten Arabic literal amounts recognition based on enhanced feature extraction model

Azzeddine Menasria; Abdelhak Bennia; Mohamed Nemissi; Moussa Sedraoui

doi:10.1117/1.JEI.27.3.033024

30 May 2018 Multiclassifiers system for handwritten Arabic literal amounts recognition based on enhanced feature extraction model

Azzeddine Menasria, Abdelhak Bennia, Mohamed Nemissi, Moussa Sedraoui

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033024 (May 2018). https://doi.org/10.1117/1.JEI.27.3.033024

Abstract

This paper proposes a recognition system for handling Arabic literal amounts based on three proposed strategies. First, an enhanced feature extraction model based on the part of Arabic word (PAW) analysis is introduced. It combines both the statistical features extracted through the word and the structural ones extracted through its PAWs. Second, the existing lexicon used in literal amounts is first partitioned into four subgroups according to the number of PAWs of the candidate word. Each subgroup is independently learned by a corresponding set of support vector machines (SVMs). However, several candidate words may contain some touching PAWs. For this reason, a proposed preclassification step is included and the classification of each candidate word is simultaneously done with and without correction of the touching PAWs. In both cases, the candidate sample is presented to the corresponding set of SVMs, where the two classification scores are computed. Then, the final decision is given according to the better score. Experimental results based on the Arabic handwritten database dataset confirm the effectiveness and robustness of the proposed recognition system in terms of classification accuracy, which reaches 95.91%.

Citation Download Citation

Azzeddine Menasria, Abdelhak Bennia, Mohamed Nemissi, and Moussa Sedraoui "Multiclassifiers system for handwritten Arabic literal amounts recognition based on enhanced feature extraction model," Journal of Electronic Imaging 27(3), 033024 (30 May 2018). https://doi.org/10.1117/1.JEI.27.3.033024

Received: 29 October 2017; Accepted: 6 May 2018; Published: 30 May 2018

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