Off-line recognition of totally unconstrained handwritten numeralsusing multilayer cluster neural network
Seong-Whan Lee
Dept. of Comput. Sci. & Eng., Korea Univ., Seoul;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1996
Volume: 18,
Issue: 6
On page(s): 648-652
ISSN: 0162-8828
References Cited: 23
CODEN: ITPIDJ
INSPEC Accession Number: 5312825
Digital Object Identifier: 10.1109/34.506416
Current Version Published: 2002-08-06
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 backpropagation 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
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