The authors propose a new type of recurrent neural network for handwritten character recognition. The proposed recurrent neural network differs from Jordan and Elman recurrent neural networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving discrimination and generalization power in recognizing handwritten characters. They also analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. The experimental results showed that the proposed recurrent neural network greatly improves the discrimination and generalization power.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A new type of recurrent neural network for handwritten character recognition


    Beteiligte:
    Seong-Whan Lee (Autor:in) / Young-Joon Kim (Autor:in)


    Erscheinungsdatum :

    01.01.1995


    Format / Umfang :

    357335 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A New Type of Recurrent Neural Network for Handwritten Character Recognition

    Lee, S.-W. / Kim, Y.-J. | British Library Conference Proceedings | 1995


    Handwritten Character Recognition by Extended Loop Neural Networks

    Miao, Z. / Yuan, B. / IEEE; Hong Kong Chapter of Signal Processing | British Library Conference Proceedings | 1994




    Integrated Segmentation and Recognition of Connected Handwritten Characters with Recurrent Neural Network

    Lee, S.-W. / Lee, E.-J. | British Library Conference Proceedings | 1995