Evaluating a Zoning Mechanism and Class-Modular Architecture for Handwritten Characters Recognition

  • Sandra de Avila
  • Leonardo Matos
  • Cinthia Freitas
  • João M. de Carvalho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


In this article we propose a feature extraction procedure based on directional histograms and investigate the application of a nonconventional neural network architecture, applied to the problem of handwritten character recognition. This approach is inspired on some characteristics of the human visual system, as it focus attention on high spatial frequencies and on the recognition of local features. Two architectures were tested and evaluated: a conventional MLP (Multiple Layer Perceptron) and a class-modular MLP. Experiments developed with the Letter database produced a recognition rate of 93.67% for the class-modular MLP. Other set of experiments utilized the IRONOFF database resulting in recognition rates of 89.21% and 80.75% for uppercase and lowercase characters respectively, also with the class-modular MLP.


Handwritten characters recognition Class-modular architecture Directional histogram 


  1. 1.
    Cormack, L.K., Bovik, A.l.: Computation models of early human vision in Handbook of image and video processing, p. 891. Academic Press, San Diego (2000)Google Scholar
  2. 2.
    Freitas, C.O.A., Oliveira, L.E.S., de Bortolozzi, F., Aires, S.B.K.: Handwritten character recognition using non-symmetrical perceptual zoning. International Journal of Pattern Recognition and Artificial Intelligence 21, 1–21 (2007)CrossRefGoogle Scholar
  3. 3.
    Kapp, M.N., Freitas, C.O.A., Sabourin, R.: Methodology for the design of NN-based month-word recognizers written on brazilian bank checks. International Journal of Image and Vision Computing, Image and Vision Computing 25, 40–49 (2007)CrossRefGoogle Scholar
  4. 4.
    Matos, L.N.: Utilização de redes bayesianas como agrupador de classificadores locais e global. PhD thesis, Federal University of Campina Grande, Brazil (2004)Google Scholar
  5. 5.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases, Irvine, CA: University of California, Department of Information and Computer Science (1998)Google Scholar
  6. 6.
    Oh, I.-S., Suen, C.Y.: A class-modular feedforward neural network for handwriting recognition. Pattern Recognition 35(1), 229–244 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Poisson, E., Viard-Gaudin, C., Lallican, P.M.: Multi-modular architecture based on convolutional neural networks for online handwritten character recognition. In: IEEE Proceedings of the 9th International Conference on Neural Information Processing, vol. 5, pp. 2444–2448 (2002)Google Scholar
  8. 8.
    Principe, J.C., Euliano, N.R., Lefebvre, W.C.: Neural and adaptive systems: fundamentals through simulations. John Wiley & Sons, New York, USA (1999)Google Scholar
  9. 9.
    Rifkin, R., Klautau, A.: In defense of one-vs-all classification. The Journal of Machine Learning Research 5, 101–141 (2004)MathSciNetGoogle Scholar
  10. 10.
    Shi, M., Fujisawa, Y., Wakabayashi, T., Kimura, F.: Handwritten numeral recognition using gradient and curvature of grayscale image. Pattern Recognition 35(10), 2051–2059 (2002)zbMATHCrossRefGoogle Scholar
  11. 11.
    Suen, C.Y., Guo, J., Li, Z.C.: Analysis and recognition of alphanumeric handprints by parts. IEEE Transactions on Systems, Man and Cybernetics 24(4), 614–631 (1994)CrossRefGoogle Scholar
  12. 12.
    Trier, O., Jain, A., Taxt, T.: Feature extraction methods for character recognition – a survey. Pattern Recognition 29(4), 641–662 (1996)CrossRefGoogle Scholar
  13. 13.
    Viard-Gaudin, C., Lallican, P.M., Binter, P., Knerr, S., The, I.R.E.S.T.E.: on/off IRONOFF) dual handwriting database. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition, pp. 455–458, Bangalore, India (1999)Google Scholar
  14. 14.
    Zhang, G.P.: Neural networks for classification: a survey. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews 30(4), 451–462 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sandra de Avila
    • 1
  • Leonardo Matos
    • 1
  • Cinthia Freitas
    • 2
  • João M. de Carvalho
    • 3
  1. 1.Federal University of Sergipe, Comp. Depart., São Cristóvão, SEBrazil
  2. 2.Pontifical Catholic University of Parana, Curitiba, PRBrazil
  3. 3.Federal University of Campina Grande, Campina Grande, PBBrazil

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