Cut Digits Classification with k-NN Multi-specialist

  • Fernando Boto
  • Andoni Cortés
  • Clemente Rodríguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)

Abstract

A multi-classifier formed by specialised classifiers for noise produced by an image is shown in this work. A study has been carried out in the case of cut images, where tree cases of specialization are considered. Classifiers based on neighbourhood criteria are used, the zoning global feature and the Euclidean distance too. Furthermore, the paper explains a modification of the Euclidean distance for classifying cut digits. The experiments have been carried out with images of typewritten digits, taken from real forms. Trying to obtain a strong database to support the experiments, we have cut images deliberately. The recognition rate improves from 84.6% to 97.70%, but whether the system provides information about the disturbance of the image, it can achieve a 98.45%.

Keywords

Recognition Rate Input Pattern Handwritten Digit Multiple Classifier System Handwritten Digit Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Whichello, A.P., Yan, H.: Linking broken character borders with variable sized masks to improve recognition. Pattern Recognition 29(8), 1429–1435 (1996)CrossRefGoogle Scholar
  2. 2.
    Rodriguez, C., Muguerza, M., Navarro, M., Zárate, A., Martín, J.I., Pérez, J.M.: A two-stage classifer for broken and blurred digits in forms. In: ICPR 1998, Brisbane, Australia, vol. 2, pp. 1101–1105 (1998)Google Scholar
  3. 3.
    Omachi, S., Sun, F., Hirotomo, A.: A noise-adaptive discriminant function and its application to blurred machine-printed kanji recognition. IEEE Transactions PAMI 22(3), 314–319 (2000)Google Scholar
  4. 4.
    Kittler, J., Hated, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions PAMI 20(3), 226–239 (1998)Google Scholar
  5. 5.
    Rodriguez, C., Soraluze, I., Muguerza, J., Martín, J.I., Álvarez, G.: Hierarchical classifiers based on neighbourhood criteria with adaptive computational cost. Pattern Recognition 35(12), 2761–2769 (2002)MATHCrossRefGoogle Scholar
  6. 6.
    Alpaydin, E., Kaynak, C., Alimoglu, F.: Cascading multiple classifiers and representations for optical and pen-based handwritten digit recognition. In: 7th IWFHR, Amsterdam, pp. 453–462 (2000)Google Scholar
  7. 7.
    Ho, T.K., Hull, J.J., Srihari, S.: Decision combination in multiple classifier systems. IEEE Transactions PAMI 16(1), 66–75 (1994)Google Scholar
  8. 8.
    Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36(1-2), 105–139 (1999)CrossRefGoogle Scholar
  9. 9.
    Aksela, M., Girdziusas, R., Laaksonen, J., Oja, E., Kangas, J.: Class-confidence critic combining. In: 8th IWFHR, Ontario, Canada, pp. 201–206 (2002)Google Scholar
  10. 10.
    Ha, T.M., Bunke, H.: Off-line, handwritten numeral recognition by pertubation method. IEEE Transactions PAMI 19(5), 535–539 (1997)Google Scholar
  11. 11.
    Erp, M.v., Vuurpijil, L., Shomaker, L.: An overview and comparison of voting methods for pattern recognition. In: 8th IWFHR, Ontario, Canada, pp. 195–200 (2002)Google Scholar
  12. 12.
    Yu, D., Yan, H.: Reconstruction of broken handwritten digits based on structural morphological features. Pattern Recognition 34, 235–254 (2001)MATHCrossRefGoogle Scholar
  13. 13.
    Wang, J., Yan, H.: Mending broken handwriting with a macrostructure analysis method to improve recognition. Pattern Recognition Letters 20, 855–864 (1999)CrossRefGoogle Scholar
  14. 14.
    Cortés, A., Boto, F., Rodriguez, C.: Noisy digit classification with multiple specialist. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3686, pp. 601–608. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Dasarathy, B.V.: Nearest neighbor (nn) norms: Nn pattern classification techniques. I. C. S. Press (1991)Google Scholar
  16. 16.
    Devroye, L., Györfi, L., Lugosi, G.: A probabilistic theory of pattern recognition. Springer, N. Y (1996)MATHGoogle Scholar
  17. 17.
    Rahman, A.F.R., Fairhurst, M.C.: Multiple classifier decision combination strategies for character recognition: A review. International Journal on Document Analysis and Recognition 5(4), 166–194 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fernando Boto
    • 1
  • Andoni Cortés
    • 1
  • Clemente Rodríguez
    • 1
  1. 1.Computer Architecture and Technology Department, Computer Science FacultyUPV/EHUSan SebastianSpain

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