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Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition

Alkhateeb, JH, Pauplin, O, Ren, J and Jiang, J (2011) Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition Knowledge-Based Systems, 24 (5). pp. 680-688.

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Abstract

This paper presents a comparative study of two machine learning techniques for recognizing handwrittenArabicwords, where hiddenMarkovmodels (HMMs) and dynamicBayesiannetworks (DBNs) were evaluated. The work proposed is divided into three stages, namely preprocessing, feature extraction and classification. Preprocessing includes baseline estimation and normalization as well as segmentation. In the second stage, features are extracted from each of the normalized words, where a set of new features for handwrittenArabicwords is proposed, based on a sliding window approach moving across the mirrored word image. The third stage is for classification and recognition, where machine learning is applied using HMMs and DBNs. In order to validate the techniques, extensive experiments were conducted using the IFN/ENIT database which contains 32,492 Arabicwords. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity.

Item Type: Article
Authors :
NameEmailORCID
Alkhateeb, JHUNSPECIFIEDUNSPECIFIED
Pauplin, OUNSPECIFIEDUNSPECIFIED
Ren, JUNSPECIFIEDUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Date : 2011
Identification Number : 10.1016/j.knosys.2011.02.008
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:25
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835285

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