Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition
Alkhateeb, Jawad H. and Pauplin, Olivier and Ren, Jinchang and Jiang, Jianmin (2011) Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowledge Based Systems, 24 (5). pp. 680-688. ISSN 0950-7051 (https://doi.org/10.1016/j.knosys.2011.02.008)
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Abstract
This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (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 handwritten Arabic words 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 Arabic words. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity.
ORCID iDs
Alkhateeb, Jawad H., Pauplin, Olivier, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194 and Jiang, Jianmin;-
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Item type: Article ID code: 48364 Dates: DateEvent1 July 2011PublishedSubjects: Science > Mathematics > Electronic computers. Computer science
Technology > Electrical engineering. Electronics Nuclear engineeringDepartment: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 30 May 2014 10:39 Last modified: 11 Nov 2024 10:42 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/48364