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

Jawad H. Alkhateeb, Olivier Pauplin, Jinchang Ren, Jianmin Jiang

Research output: Contribution to journalArticle

39 Citations (Scopus)
86 Downloads (Pure)

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. 

Original languageEnglish
Pages (from-to)680-688
Number of pages9
JournalKnowledge Based Systems
Volume24
Issue number5
DOIs
Publication statusPublished - 1 Jul 2011

Keywords

  • off-line handwritten recognition
  • hidden markov model (HMM)
  • dynamic bayesian network (DBN)
  • performance evaluation
  • IFN/ENIT database

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