Skip to main content
SpringerLink
Log in

Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition

  • Conference paper
Multiple Classifier Systems (MCS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5997))

Included in the following conference series:

Abstract

The first observation concerning Arabian manuscript reveals the complexity of the task, especially for the used classifiers ensemble. One of the most important steps in the design of a multi-classifier system (MCS), is the its components choice (classifiers). This step is very important to the overall MCS performance since the combination of a set of identical classifiers will not outperform the individual members. To select the best classifier set from a pool of classifiers, the classifier diversity is the most important property to be considered. The aim of this paper is to study Arabic handwriting recognition using MCS optimization based on diversity measures. The first approach selects the best classifier subset from large classifiers set taking into account different diversity measures. The second one chooses among the classifier set the one with the best performance and adds it to the selected classifiers subset. The performance in our approach is calculated using three diversity measures based on correlation between errors. On two database sets using 9 different classifiers, we then test the effect of using the criterion to be optimized (diversity measures,), and fusion methods (voting, weighted voting and Behavior Knowledge Space). The experimental results presented are encouraging and open other perspectives in the classifiers selection field especially speaking for Arabic Handwritten word recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cheriet, M., Bunke, H., Hu, J.: NewFrontiers in Handwriting Recognition. Pattern Recognition, Editorial (2009)

    Google Scholar 

  2. Giacinto, G., Roli, F.: An approach to the automatic design of multiple classifier systems. Pattern Recognition Letters 22(1), 25–33 (2001)

    Article  MATH  Google Scholar 

  3. Aksela, M.: Comparison of Classifier Selection Methods for Improving Committee Performance. In: Multiple Classifier Systems, UK, pp. 84–93 (2003)

    Google Scholar 

  4. Xu, l., Krzyzak, A., Suen, C.Y.: Methodes of combining multiple classifiers and their applications to handwritten recognition. IEEE trans. Syst., Man, Cybern. SMC-22(3), 418–435 (1992)

    Article  Google Scholar 

  5. Czyz, J., Sadeghi, M., Kittler, J., Vandendorpe, L.: Decision fusion for face authentication. In: Proc. First Internat. Conf. on Biometric Authentication, pp. 686–693 (2004)

    Google Scholar 

  6. Zhou, D., Zhang, J.: Face recognition by combining several algorithms. Pattern Recognition 3(3), 497–500 (2002)

    Google Scholar 

  7. Canuto, A.M.P., Abreu, M.C.C., Oliveira, L.M., Xavier, J.C.: Using weighted dynamic classifier selection methods in ensembles with different levels of diversity. Int. J. Hybrid Intell. Syst. 3(3), 147–158 (2006)

    MATH  Google Scholar 

  8. Farah, N., Souici, L., Sellami, M.: Classifiers combination and syntax analysis for arabic literal amount recognition. Engineering Applications of Artificial Intelligence 19(1) (2006)

    Google Scholar 

  9. Azizi, N., Farah, N., Khadir, M., Sellami, M.: Arabic Handwritten Word RecognitionvUsing Classifiers Selection and features Extraction / Selection. In: 17th IEEE Conference in Intelligent Information System, IIS 2009, Proceedings of Recent Advances in Intelligent Information Systems, pp. 735–742. Academic Publishing House, Warsaw (2009)

    Google Scholar 

  10. Santos, A.M.: Investigating the influence of the choice of the ensemblemembers in accuracy and diversity of selection-based and fusion-based methods for ensembles. Pattern Recognition Letters 28, 472–486 (2007)

    Article  Google Scholar 

  11. Ulas, A., Semerci, M., Yıldız, O.T., Alpaydın, E.: Incremental construction of classifier and discriminant ensembles. Information Sciences 179, 1298–1318 (2009)

    Article  Google Scholar 

  12. Aksela, M., Laaksonen, J.: Using diversity of errors for selecting members of a committee classifier. Pattern Recognition 39, 608–623 (2006)

    Article  MATH  Google Scholar 

  13. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley–Interscience, Chichester (2004)

    Book  MATH  Google Scholar 

  14. Amin, A.: Off-line Arabic character recognition: The state of the art. Pattern Recognition 31(5), 517–530 (1998)

    Article  MathSciNet  Google Scholar 

  15. Ben Amara, N., Belaid, A.: Printed PAW Recognition based on planar Hidden Marcov Model. In: Proceedings of the 13th International Conference on Pattern Recognition, ICPR, vol. 2, pp. 220–226 (1998)

    Google Scholar 

  16. Al-Badr, B., Mohmoud, S.A.: Survey and bibliography of Arabic optical text recognition. Signal Processing 41, 49–77 (1995)

    Article  MATH  Google Scholar 

  17. Benouareth, A., Ennaji, A., Sellami, M.: Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration. EURASIP, Journal on Advances in Signal Processing 2008, Article ID 247354 (2008)

    Google Scholar 

  18. Azizi, N., Sari, T., Souici, L., Sellami, M.: Une architecture de combinaison floue de classifieurs neuronaux pour la reconnaissance de mots arabes manuscrits. In: CIFED 2002, Hammamet, Tunisie, pp. 89–96 (2002)

    Google Scholar 

  19. Azizi, N., Khadir, M.T., Sellami, M.: Un système HMM pour le tri postal algérien. In: 9th Maghrebian Conference on Information Technologies MCSEAI, pp. 256–262 (2006)

    Google Scholar 

  20. Alkoot, F.M., Kittler, J.: Experimental evaluation of expert fusion strategies. Pattern Recognition Letters 20, 1361–1369 (1999)

    Article  Google Scholar 

  21. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE transactions on pattern analysis and machine intelligence 20(3) (1998)

    Google Scholar 

  22. Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)

    Article  MATH  Google Scholar 

  23. Ruta, D., Gabrys, B.: Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting. In: Multiple Classifier Systems, pp. 399–408 (2001)

    Google Scholar 

  24. Jain, A., Duin, R., Mao, J.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)

    Article  Google Scholar 

  25. Ko, A.H.R., Sabourina, R., Britto, A., Oliveira, L.: Pairwise fusion matrix for combining classifiers. Pattern Recognition 40, 2198–2210 (2007)

    Article  MATH  Google Scholar 

  26. Raudys, S., Roli, F.: The Behavior Knowledge Space Fusion Method: Analysis of Generalization Error and Strategies for Performance Improvement (2006)

    Google Scholar 

  27. Dara, R.A.: Cooperative Training in Multiple Classifier Systems; thesis: Doctor of Philosophy; Waterloo, Ontario, Canada (2007)

    Google Scholar 

  28. Benouareth, A., Ennaji, A., Sellami, M.: Semi-continuous HMMs with explicit state duration for unconstrained Arabic word modeling and recognition. Pattern Recognition Letters 29(12), 1742–1752 (2008)

    Article  Google Scholar 

  29. Ruta, D., Gabrys, B.: A theoretical analysis of the limits of majority voting errors for multiple classifier systems. Pattern Anal. Appl. 5(4), 333–350 (2002)

    Article  MathSciNet  Google Scholar 

  30. Aksela, M.: Adaptive combinations of classifiers with application to on-line handwritten.character recognition, dissertations in computer and information science report d19, espoo (2007)

    Google Scholar 

  31. Kapp, M.N., Sabourin, R., Maupin, P.: An Empirical Study on Diversity Measures and Margin Theory for Ensembles of Classifiers. In: 10th International Conference on Information Fusion, pp. 1–8 (2007)

    Google Scholar 

  32. Kohavi, H., Wolpert, D.H.: Bias plus variance decomposition for zero-one loss functions. In: Saitta, L. (ed.) Proceedings of the Thirteenth International Conference on Machine Learning, pp. 275–283 (1996)

    Google Scholar 

  33. Pechwitz, M., Maergner, V.: HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENIT Database. In: ICDAR, Scotland, pp. 890–894 (2003)

    Google Scholar 

  34. Suen, C.Y., Guo, J., Li, Z.C.: Analysis and recognition of alphanumeric handprints by parts. IEEE Trans. on Systems, Man, and Cybernetics 24(4), 614–631 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Labged, Department of Computer Science, Badji Mokhtar University, Annaba, Algeria

    Nabiha Azizi & Nadir Farah

  2. Lri, Department of Computer Science, Badji Mokhtar University of Annaba, Algeria

    Mokhtar Sellami

  3. Litis, Laboratoire d’Informatique, de Traitement de l’Information et des Systèmes, EA, 4108

    Abdel Ennaji

Authors
  1. Nabiha Azizi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadir Farah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mokhtar Sellami
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdel Ennaji
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Informatics Science, Nile University, 12677, Giza, Egypt

    Neamat El Gayar

  2. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Josef Kittler

  3. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Azizi, N., Farah, N., Sellami, M., Ennaji, A. (2010). Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition. In: El Gayar, N., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2010. Lecture Notes in Computer Science, vol 5997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12127-2_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12127-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12126-5

  • Online ISBN: 978-3-642-12127-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation