Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

  • Authors:

  • Jawad H AlKhateeb;Jinchang Ren;Jianmin Jiang;Husni Al-Muhtaseb

  • Affiliations:

  • Faculty of Science and Information Technology, Al-Zaytoona University of Jordan, Amman, Jordan;Centre for excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XW, United Kingdom;School of Informatics, University of Bradford, Bradford BD7 1DP, United Kingdom;Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2011

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Abstract

Recognition of handwritten Arabic cursive texts is a complex task due to the similarities between letters under different writing styles. In this paper, a word-based off-line recognition system is proposed, using Hidden Markov Models (HMMs). The method employed involves three stages, namely preprocessing, feature extraction and classification. First, words from input scripts are segmented and normalized. Then, a set of intensity features are extracted from each of the segmented words, which is based on a sliding window moving across each mirrored word image. Meanwhile, structure-like features are also extracted including number of subwords and diacritical marks. Finally, these features are applied in a combined scheme for classification. Intensity features are used to train a HMM classifier, whose results are re-ranked using structure-like features for improved recognition rate. In order to validate the proposed techniques, extensive experiments were carried out using the IFN/ENIT database which contains 32,492 handwritten Arabic words. The proposed algorithm yields superior results of improved accuracy in comparison with several typical methods.