Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network

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122 Citations (Scopus)

Abstract

In this paper, we propose a new scheme for off-line recognition of totally unconstrained handwritten numerals using a simple multilayer cluster neural network trained with the back propagation algorithm and show that the use of genetic algorithms avoids the problem of finding local minima in training the multilayer cluster neural network with gradient descent technique, and improves the recognition rates. In the proposed scheme, Kirsch masks are adopted for extracting feature vectors and a three-layer cluster neural network with five independent subnetworks is developed for classifying similar numerals efficiently. In order to verify the performance of the proposed multilayer cluster neural network, experiments with handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. For the case of determining the initial weights using a genetic algorithm, 97.10%, 99.12%, and 99.40% correct recognition rates were obtained, respectively.

Original languageEnglish
Pages (from-to)648-652
Number of pages5
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume18
Issue number6
DOIs
Publication statusPublished - 1996

Keywords

  • Genetic algorithm
  • Multilayer cluster neural network
  • Totally unconstrained handwritten numeral recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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