Cut Digits Classification with k-NN Multi-specialist

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


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%.


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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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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