This paper presents experiments with genetically engineered feature sets for recognition of online handwritten characters. These representations stem from a nondescript decomposition of the character frame into a set of rectangular regions, possibly overlapping each represented by a vector of 7 fuzzy variables. Efficient new feature sets are automatically discovered using genetic programming techniques. Recognition experiments conducted on isolated digits of the Unipen database yield improvements of more than 3% over a previously, manually designed representation where region positions and sizes were fixed.


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    Titel :

    Genetic engineering of handwriting representations


    Beteiligte:
    Lemieux, A. (Autor:in) / Gagne, C. (Autor:in) / Parizeau, M. (Autor:in)


    Erscheinungsdatum :

    2002-01-01


    Format / Umfang :

    325754 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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