Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network

Seong Whan Lee, Chang Hun Kim, Hong Ma, Yuan Y. Tang

    Research output: Contribution to journalArticlepeer-review

    52 Citations (Scopus)

    Abstract

    In this paper, we propose a new scheme for multiresolution recognition of unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: a feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, Electro-Technical Laboratory of Japan, and Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.

    Original languageEnglish
    Pages (from-to)1953-1961
    Number of pages9
    JournalPattern Recognition
    Volume29
    Issue number12
    DOIs
    Publication statusPublished - 1996 Dec

    Keywords

    • Handwritten numeral recognition
    • Multilayer cluster neural network
    • Multiresolution recognition
    • Wavelet transform

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Computer Vision and Pattern Recognition
    • Artificial Intelligence

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