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Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach

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Abstract

This paper describes a new approach to combine a multilayer perceptron (MLP) and a hidden Markov model for recognizing handwritten Arabic words. As a first step, connected components (CCs) of black pixels are detected, then the system determines which CCs are sub-words and which are diacritics. The diacritics are then isolated and identified separately, and the sub-words are segmented into graphemes. The MLP is used as labeller (classifier) and probability estimator. We also introduce the diacritics and their positions in our hybrid system; thus, only one model including both grapheme and diacritic states is built to represent the whole alphabet. Finally, we consider a maximum likelihood classifier to decide about the word class. The experiments that were performed show promising results on Arabic word segmentation and recognition.

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References

  1. Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE. 1989;77(2):257–86.

    Article  Google Scholar 

  2. Chen MY, Kundu A, Srihari SN. Variable duration hidden Markov and morphological segmentation for handwritten word recognition. IEEE Trans Image Process. 1995;4(12):1675–88.

    Article  PubMed  CAS  Google Scholar 

  3. Senior AW, Robinson AJ. An off-line cursive handwriting recognition system. IEEE Trans Pattern Anal Mach Intell. 1998;20(3):309–21.

    Article  Google Scholar 

  4. Altuwaijri M, Bayoumi M. Arabic text recognition using neural networks. In: Proceedings of international symposium on circuits and systems—ISCAS’94; 1994. p. 415–8.

  5. Amin A, Al-Sadoun H. Handprinted Arabic character recognition system using an artificial neural network. Pattern Recognit. 1996;29:663–75.

    Article  Google Scholar 

  6. Morgan N, Bourlard H. Continuous speech recognition using multilayer perceptrons with hidden Markov models. In: Proceedings of ICASSP-90; 1990. p. 413–6.

  7. Lorigo LM, Govindaraju V. Offline Arabic handwriting recognition: a survey. IEEE Trans Pattern Anal Mach Intell. 2006;28(5):712–24.

    Article  PubMed  Google Scholar 

  8. Makhoul J, Schwartz R, Lapre C, Bazzi I. A script independent methodology for optical character recognition. Pattern Recognit. 1998;31(9):1285–94.

    Article  Google Scholar 

  9. Dehghan M, Faez K, Ahmadi M, Shridhar M. Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM. Pattern Recognit. 2001;34:1057–65.

    Article  Google Scholar 

  10. Khorsheed MS. Recognising handwritten Arabic manuscripts using a single hidden Markov model. Pattern Recognit Lett. 2003;24(14):2235–42.

    Article  Google Scholar 

  11. Amin A, Mari JF. Machine recognition and correction of printed Arabic text. IEEE Trans Man Cybern. 1989;9:1300–6.

    Article  Google Scholar 

  12. Miled H, Olivier C, Cheriet M, Lecourtier Y. Coupling observations/letters for a markovian modeling applied to the recognition of the Arabic handwriting. In: Proceedings of 4th IAPR international conference on document analysis and recognition, ICDAR’97, Ulm, Germany; 1997. p. 580–3.

  13. Menasri F, Vincent N, Augustin E, Cheriet M. Shape-based alphabet for off-line Arabic handwriting recognition. In: Proceedings of the 9th international conference on document analysis and recognition ICDAR, Curitiba, Brazil; 2007. p. 969–73.

  14. Boukharouba A, Bennia A. Recognition of Handwritten Arabic words using a neuro-fuzzy network. In: Proceedings of AIP 1st mediterranean conference on intelligent systems and automation, Annaba; 2008. p. 254–9.

  15. Farah N, Souici L, Sellami M. Classifiers combination and syntax analysis for Arabic literal amount recognition. Eng Appl Artif Intell. 2006;19:29–39.

    Article  Google Scholar 

  16. Benouareth A, Ennaji A, Sellami M. Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition. Pattern Recognit Lett. 2008;29:1742–52.

    Article  Google Scholar 

  17. Morita M, Sabourin R, Bortolozzi F, Suen CY. Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach. Int J Doc Anal Recognit. 2004;6:248–62.

    Article  Google Scholar 

  18. Al-Yousefi H, Udpa SS. Recognition of Arabic characters. IEEE Trans Pattern Anal Mach Intell. 1992;14:853–7.

    Article  Google Scholar 

  19. El-Yacoubi A, Gilloux M, Sabourin R, Suen CY. An HMM-based approach for off-line unconstrained handwritten word modeling and recognition. IEEE Trans Pattern Anal Mach Intell. 1999;21(8):752–60.

    Article  Google Scholar 

  20. Lethelier E, Leroux M, Gilloux M. Traitement des montants numériques des chèques postaux, approche d’une méthode de segmentation basée sur la reconnaissance. Actes de CNED 94 (3ème Colloque National sur l’Ecrit et le Document), Rouen, France; 1994. p. 315–23.

  21. Naik JM, Lubensky DM. A hybrid HMM-MLP speaker verification algorithm for telephone speech. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing (ICASSP); 1994. p. 153–6.

  22. Looney CG. Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng. 1996;8(2):211–26.

    Article  Google Scholar 

  23. Bourlard H, Wellekens CJ. Links between Markov models and multilayer perceptrons. In: Denver CO, Touretzky D, editors. Proceedings of IEEE conference on neural information processing systems, Morgan-Kaufmann; 1989. p. 502–10.

  24. Hampshire JB, Pearlmutter H. Equivalence proofs for multi-layer perceptron classifiers and the Bayesian discriminant function. In: Proceedings of in connectionist models: proceedings of the summer school, Morgan-Kauffmann; 1990. p. 159–72.

  25. Ha TM, Bunk H. Off-line handwritten numeral recognition by perturbation method. IEEE Trans Pattern Anal Mach Intell. 1997;19(5):535–9.

    Article  Google Scholar 

  26. Oliveira LS, Sabourin R, Bortolozzi F, Suen CY. Automatic recognition of handwritten numerical strings: a recognition and verification strategy. IEEE Trans Pattern Anal Mach Intell. 2002;24(11):1438–54.

    Article  Google Scholar 

  27. Liu J, Gader P. Neural networks with enhanced outlier rejection ability for off-line handwritten word recognition. Pattern Recognit. 2002;35:2061–71.

    Article  Google Scholar 

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Authors and Affiliations

  1. Département d’Electronique et Télécommunications, Faculté des Sciences et de la Technologie, Université 8 Mai 1945 Guelma, BP 401, Guelma, 24000, Algeria

    Abdelhak Boukharouba

  2. Département d’Electronique, Faculté Des Sciences de l’Ingénieur, Université Mentouri de Constantine, Constantine, Algeria

    Abdelhak Bennia

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  1. Abdelhak Boukharouba
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  2. Abdelhak Bennia
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Correspondence to Abdelhak Boukharouba.

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Boukharouba, A., Bennia, A. Recognition of Handwritten Arabic Literal Amounts Using a Hybrid Approach. Cogn Comput 3, 382–393 (2011). https://doi.org/10.1007/s12559-010-9088-6

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  • DOI: https://doi.org/10.1007/s12559-010-9088-6

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