DocumentCode
3174117
Title
Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network
Author
Lee, Seong-Whan ; Kim, Young Joon
Author_Institution
Dept. of Comput. Sci., Chungbuk Nat. Univ., Cheongju, South Korea
Volume
2
fYear
1994
fDate
9-13 Oct 1994
Firstpage
507
Abstract
In this paper, we propose a simple multilayer cluster neural network with five independent subnetworks for off-line recognition of totally unconstrained handwritten numerals. We also show that the use of genetic algorithms for avoiding the problem of finding local minima in training the multilayer cluster neural network with gradient descent technique reduces error rates
Keywords
optical character recognition; error rate reduction; genetic algorithms; gradient descent technique; local minima; multilayer cluster neural network; off-line recognition; totally unconstrained handwritten numerals; Character recognition; Detectors; Feature extraction; Genetic algorithms; Handwriting recognition; Image coding; Image edge detection; Multi-layer neural network; Neural networks; Nonhomogeneous media;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on
Conference_Location
Jerusalem
Print_ISBN
0-8186-6270-0
Type
conf
DOI
10.1109/ICPR.1994.576997
Filename
576997
Link To Document