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

  • Authors:

  • Jawad H. AlKhateeb;Olivier Pauplin;Jinchang Ren;Jianmin Jiang

  • Affiliations:

  • Faculty of Science and Information Technology, Al-Zaytoona University of Jordan, Amman, Jordan;School of Informatics, University of Bradford, Bradford BD7 1DP, United Kingdom;Centre for excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom;School of Informatics, University of Bradford, Bradford BD7 1DP, United Kingdom

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2011

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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.