This paper presents a system that overcomes the dependence on pattern transformation, like translation, rotation, scaling and further deformations of the input to a recognition system, by reducing the pattern to a normal form. The reduction may be viewed as pre-processing that uses different algorithms to reduce the pattern to normal form at: 0, 1, 2, .., n-level. Our system performs, on patterns representing binary images of characters, the reduction to a normal pattern of level 0, 1 and 2, that in practice correspond, respectively, to character extraction, scaling and rotation until the recovery of a standard condition for these. The patterns so normalised are supplied as input to a recognition system, constituted by a Hintzman neural network, that is a content-addressable-memory, which has well known problems of sensitivity to the input variations.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Using normal patterns in handwritten character recognition


    Contributors:
    Tascini, G. (author) / Montesanto, A. (author) / Puliti, P. (author)


    Publication date :

    1999-01-01


    Size :

    72234 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Using Normal Patterns in Handwritten Character Recognition

    Tascini, G. / Montesanto, A. / Puliti, P. et al. | British Library Conference Proceedings | 1999


    Fuzzy Logic in Handwritten Character Recognition

    Kiselev, V. V. | British Library Online Contents | 2003


    Unconstrained Handwritten Character Recognition using Metaclasses of Characters

    Koerich, A. L. / Kalva, P. R. | British Library Conference Proceedings | 2005



    Handwritten Character Recognition Using Dynamic-Resolution Shape Analysis

    Tascini, G. / Zingaretti, P. / University of Naples et al. | British Library Conference Proceedings | 1994