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Pattern Analysis and Applications

, Volume 22, Issue 1, pp 99–113 | Cite as

Hybrid one-class classifier ensemble based on fuzzy integral for open-lexicon handwritten Arabic word recognition

  • Bilal HadjadjiEmail author
  • Youcef Chibani
  • Hassiba Nemmour
Theoretical Advances
  • 84 Downloads

Abstract

One-class classifier (OCC) is involved for solving different kinds of problems due to its ability to represent a class distribution regardless the remaining classes. Its main advantage for multi-class classification is offering an open system and therefore allows easily extending new classes without retraining OCCs. So far, hidden Markov models, support vector machines and neural networks are the most used classifiers for Arabic word recognition, which provides a system with closed lexicon. In this paper, the OCCs are explored in order to perform an Arabic word recognition system with an open lexicon. Generally, pattern recognition systems designed by a single system suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining multiple systems becomes an attractive research topic for performance and robustness enhancement. Fixed rules are commonly used us combiners for the hybrid OCC ensembles. The present paper aims to propose a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Furthermore, an alternative framework is proposed to design a parameter-independent and open-lexicon handwritten Arabic word recognition system as well as a new density measure function. Experimental results conducted on Arabic handwritten dataset using different types of OCCs with large number of classes highlight the superiority of FI for hybrid OCC ensembles.

Keywords

One-class classifiers Hybrid OCC ensemble Fuzzy integral Density measures Open-lexicon Arabic word recognition 

References

  1. 1.
    Lawgali A (2015) A survey on Arabic character recognition. Int J Signal Process Image Process Pattern Recognit 8(2):401–426Google Scholar
  2. 2.
    Shatnawi M (2015) Off-line handwritten Arabic character recognition: a survey. In: Proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV)Google Scholar
  3. 3.
    Alginahi YM (2013) A survey on Arabic character segmentation. Int J Doc Anal Recognit (IJDAR) 16(2):105–126Google Scholar
  4. 4.
    Likforman-Sulem L, Sigelle M (2008) Recognition of degraded characters using dynamic Bayesian networks. Pattern Recognit 41(10):3092–3103zbMATHGoogle Scholar
  5. 5.
    Ye Q, Doermann D (2014) Text detection and recognition in imagery: a survey. IEEE Trans Pattern Anal Mach Intell 37(7):1480–1500Google Scholar
  6. 6.
    Farah N, Souici L, Sellami M (2005) Classifiers combination and syntax analysis for Arabic literal amount recognition. Eng Appl Artif Intell 19(1):29–39Google Scholar
  7. 7.
    Knerr S, Augustin E, Baret O, Price D (1998) Hidden Markov model based word recognition and its application to legal amount reading on French checks. J Comput Vis Image Underst 70(3):404–419Google Scholar
  8. 8.
    Plötz T, Fink GA (2009) Markov models for offline handwriting recognition: a survey. Int J Doc Anal Recognit 12(4):269–298Google Scholar
  9. 9.
    Muñoz-Marí J, Camps-Valls G, Gómez-Chova L, Calpe-Maravilla J (2007) Combination of one class remote sensing image classifiers. In: International geoscience and remote sensing symposium, pp 1509–1512Google Scholar
  10. 10.
    Krawczyk B, Filipczuk P (2014) Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition. Eng Appl Artif Intell 31:126–135Google Scholar
  11. 11.
    Vinciarelli A (2002) A survey on off-line cursive word recognition. J Pattern Recognit Soc 35(7):1433–1446zbMATHGoogle Scholar
  12. 12.
    Jayech K, Mahjoub MA, Amara NEB (2016) Synchronous multi-stream hidden markov model for offline Arabic handwriting recognition without explicit segmentation. Neurocomputing 214:958–971Google Scholar
  13. 13.
    Hmeidi I, Hawashin B, El-Qawasmeh E (2008) Performance of KNN and SVM classifiers on full word Arabic articles. Adv Eng Inform 22:106–111Google Scholar
  14. 14.
    AlKhateeb JH, Pauplin O, Ren J, Jiang J (2011) Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowl Based Syst 24:680–688Google Scholar
  15. 15.
    Elleuch M, Maalej R, Kherallah M (2016) A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. In: International conference on computational science, vol 80, ICCS 2016, pp 1712–1723Google Scholar
  16. 16.
    Sun BY, Huang DS (2003) Support vector clustering for multi-class classification problems. In: The congress on evolutionary computation, Canberra, Australia, pp 1480–1485Google Scholar
  17. 17.
    Goh KS, Chang EY, Li B (2005) Using one-class and two-class SVMs for multiclass image annotation. IEEE Trans Knowl Data Eng 17(10):1333–1346Google Scholar
  18. 18.
    Ban T, Abe S (2006) Implementing multi-class classifiers by one-class classification methods. In: International joint conference on neural networks, Vancouver, Canada, pp 327–332Google Scholar
  19. 19.
    Rabaoui A, Davy M, Rossignol S, Ellouze N (2008) Using one-class SVMS and wavelets for audio surveillance. IEEE Trans Inf Forensic Secur 3(4):763–775Google Scholar
  20. 20.
    Yeh CY, Lee ZY, Lee SJ (2009) Boosting one-class support vector machines for multi-class classification. Appl Artif Intell 23(4):297–315Google Scholar
  21. 21.
    Boehm O, Hardoon DR, Manevitz LM (2011) Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. Int J Mach Learn Cybernet 2:125–134Google Scholar
  22. 22.
    Tax DMJ (2001) One-class classification, PhD thesis, Delft University of Technology, NetherlandsGoogle Scholar
  23. 23.
    Kwang-Kyu S (2007) An application of one-class support vector machines in content-based image retrieval. Expert Syst Appl 33(2):491–498Google Scholar
  24. 24.
    Manevitz L, Yousef M (2007) One-class document classification via neural networks. Neurocomputing 70:1466–1481Google Scholar
  25. 25.
    Bergani C, Oliveira LS, Koreich AL, Sabourin R (2009) Combining different biometric traits with one-class classification. Signal Process 89:2117–2127zbMATHGoogle Scholar
  26. 26.
    Kwak K-C, Pedrycz W (2005) Face recognition: a study in information fusion using fuzzy integral. Pattern Recognit Lett 26(26):719–733Google Scholar
  27. 27.
    Chiang JH, Gaber PD (1997) Hybrid fuzzy-neural systems in handwritten word recognition. IEEE Trans Fuzzy Syst 5:497–510Google Scholar
  28. 28.
    Pham T, Wagner M (2000) Similarity normalization for speaker verification by fuzzy fusion. Pattern Recognit 33:309–315Google Scholar
  29. 29.
    Chiang JH (1999) Choquet fuzzy integral-based hierarchical networks for decision analysis. IEEE Trans Fuzzy Syst 7:63–71Google Scholar
  30. 30.
    Cabrera JBD, Gutiérrez C, Mehra RK (2008) Ensemble methods for anomaly detection and distributed intrusion detection in mobile ad-hoc networks. Inf Fusion 9:96–119Google Scholar
  31. 31.
    Juszczak P, Duin RPW (2004) Combining one-class classifiers to classify missing data. In: 5th international workshop, multiple classifier systems. Cagliari, Italy, pp 92–101Google Scholar
  32. 32.
    Krawczyk B, Wozniak M (2014) Diversity measures for one-class classifier ensembles. Neurocomputing 126:36–44Google Scholar
  33. 33.
    Cyganek B, Krawczyk B (2015) Data classification with ensembles of one-class support vector machines and sparse nonnegative matrix factorization. In: 7th Asian conference on intelligent information and database systems, Bali, Indonesia, pp 526–535Google Scholar
  34. 34.
    Kuncheva L (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience Publication, HobokenzbMATHGoogle Scholar
  35. 35.
    Abbas N, Chibani Y, Belhadi Z, Hedir M (2013) A DSmT based combination scheme for multiclass classification. In: 16th international conference on information fusion, Istanbul, Turkey, pp 1950–1957Google Scholar
  36. 36.
    Hadjadji B, Chibani Y, Nemmour H (2014) Fuzzy integral combination of one-class classifiers designed for multi-class classification. In: 11th international conference on image analysis and recognition, Vilamoura, Portugal, pp 320–328Google Scholar
  37. 37.
    Zhang Y, Zhang B, Coenen F, Xiao J, Lu W (2014) One-class kernel subspace ensemble for medical image classification. EURASIP J Adv Signal Process 17:1–13Google Scholar
  38. 38.
    Krawczyk B, Woźniak M, Cyganek B (2014) Clustering-based ensembles for one-class classification. Inf Sci 264:182–195MathSciNetzbMATHGoogle Scholar
  39. 39.
    Cho S-B, Kim JH (1995) Combining multiple neural networks by fuzzy integrals for robust classification. IEEE Trans Syst Man Cybern 25(2):380–384Google Scholar
  40. 40.
    Cho S-B (1995) Fuzzy, aggregation of modular neural networks with ordered weighted averaging operators. Int J Approx Reason 13(4):359–375zbMATHGoogle Scholar
  41. 41.
  42. 42.
    Rath TM, Manmatha R (2003) Features for word spotting in historical manuscripts. In: 7th international conference on document analysis and recognition, vol 1, Edinburgh, Scotland, August 3–6 2003, pp 218–222Google Scholar
  43. 43.
    Bluche T, Ney H, Kermorvant C (2013) Feature extraction with convolutional neural networks for handwritten word recognition. In: 12th international conference on document analysis and recognition (ICDAR), pp 285–289Google Scholar
  44. 44.
    Poznanski A, Wolf L (2016) Cnn-n-gram for handwriting word recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2305–2314Google Scholar
  45. 45.
    Candès E, Demanet L, Donoho DL, Ying L (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5:861–899MathSciNetzbMATHGoogle Scholar
  46. 46.
    Starck J, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684MathSciNetzbMATHGoogle Scholar
  47. 47.
    Mandal T, Wu QMJ (2008) Face recognition using curvelet based PCA. In: International conference on pattern recognition, Florida, USA, pp 1–4Google Scholar
  48. 48.
    Saha A, Wu QJ (2010) Facial expression recognition using curvelet based local binary patterns. In: IEEE international conference on acoustics speech and signal processing, Texas, USA, pp 2470–2473Google Scholar
  49. 49.
    Majumdar A (2009) Image compression by sparse PCA coding in curvelet domain. SIViP 3(1):27–34zbMATHGoogle Scholar
  50. 50.
    Arivazhagan S, Ganesan L, Kumar TGS (2006) Texture classification using curvelet statistical and co-occurrence features. In: International conference on pattern recognition, Hong Kong, pp 938–941Google Scholar
  51. 51.
    Sumana IJ, Islam MM, Zhang D, Lu G (2008) Content based image retrieval using curvelet transform. In: The 10th workshop on multimedia signal processing, Australia, pp 11–16Google Scholar
  52. 52.
    Majumdar A (2006) Bangla basic character recognition using digital curvelet transform. J Pattern Recognit Res 2(1):17–26Google Scholar
  53. 53.
    Kazemi FM, Izadian J, Moravejian R, Kazemi EM (2008) Numeral recognition using curvelet transform. In: IEEE/ACS international conference on computer systems and applications, Doha, Qatar, pp 606–612Google Scholar
  54. 54.
    Shirdhonkar MS, Kokare M (2011) Off-line handwritten signature retrieval using Curvelet transforms. Int J Comput Eng 3(4):1658–1665Google Scholar
  55. 55.
    Guerbai Y, Chibani Y, Hadjadji B (2015) The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recognit 48(1):103–113Google Scholar
  56. 56.
    Favata J, Srikantan G (1996) A multiple feature/resolution approach to handprinted digit and character recognition. Int J Imaging Syst Technol 7(4):304–311Google Scholar
  57. 57.
    Duin RPW (2002) The combining classifier: to train or not to train? In: 16th international conference on pattern recognition, Canada, pp 765–770Google Scholar
  58. 58.
    Dietterich TG (1998) Approximate statistical tests for combining supervised classification learning algorithms. Neural Comput 10(7):1895–1923Google Scholar
  59. 59.
    Nemmour H, Chibani Y (2011) Handwritten Arabic word recognition based on ridgelet transform and support vector machines. In: International conference on high performance computing and simulation, HPCS, pp 357–361Google Scholar
  60. 60.
    Khalifa M, BingRu Y (2011) A novel word based Arabic handwritten recognition system using SVM classifier. In: Shen G, Huang X (eds) ECWAC 2011, part I. CCIS, vol 143. Springer, Heidelberg, pp 163–171Google Scholar
  61. 61.
    Alalshekmubarak A, Hussain A, Wan QF (2012) Off-line handwritten Arabic word recognition using SVMs with normalized poly kernel. Neural Inf Process Lect Notes Comput Sci 7664:85–91Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Bilal Hadjadji
    • 1
    Email author
  • Youcef Chibani
    • 1
  • Hassiba Nemmour
    • 1
  1. 1.Laboratoire d’Ingénierie des Systèmes Intelligents et Communicants (LISIC), Faculty of Electronics and Computer ScienceUniversity of Science and Technology Houari Boumediene (USTHB)AlgiersAlgeria

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