A new classification scheme for handwritten digit recognition is proposed. The method is based on combining the decisions of two multilayer perceptron (MLP) artificial neural network classifiers operating on two different feature types. The first feature set is defined on the pseudo Zernike moments of the image whereas the second feature type is derived from the shadow code of the image using a newly defined projection mask. A MLP network is employed to perform the combination task. The performance is tested on a data base of 15000 samples and the advantage of the combination approach is demonstrated.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Handwritten digit recognition using combination of neural network classifiers


    Contributors:
    Khofanzad, A. (author) / Chung, C. (author)


    Publication date :

    1998-01-01


    Size :

    540441 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Hand-Written Digit Recognition Using Combination of Neural Network Classifiers

    Khotanzad, A. / Chung, C. / University of Arizona et al. | British Library Conference Proceedings | 1998



    Hand Written Digit Recognition using BKS Combination of Neural Network Classifiers

    Khotanzad, A. / Chung, C. / IEEE et al. | British Library Conference Proceedings | 1994


    Handwritten Digit Recognition Based on a Neural - SVM Combination

    Nemmour, H. / Chibani, Y. | British Library Online Contents | 2010


    Multidimensional Multistage K-NN Classifiers for Handwritten Digit Recognition

    Soraluze, I. / Rodriguez, C. / Boto, F. et al. | British Library Conference Proceedings | 2002