TY - GEN
T1 - Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network
AU - Lee, Seong Whan
AU - Kim, Young Joon
N1 - Funding Information:
This research was supported by the 1992 Directed Basic Research Fund of Korea Science and Engineering Foundation.
Funding Information:
bined a genetic algorithm with the multilayer cluster neural network to avoid the problem of finding local minima in training with a gradient descent technique. Consequently, the use of a genetic algorithm reduced error rates. In this paper, we used a simple multilayer cluster neural network which has 10 output units: one per class. However, considering multiple models for the class which has wide variations, it is expected that the performance of proposed scheme will be improved. Further investigation should be made, however, to design a locally constrained cluster network architec-ture which has good generalization and involves mul-tiple models and to develop a technique in which segmentation and recognition are integrated for the recognition of unconstrained handwritten, connected numerals. Acknowledgments This research was supported by the 1992 Directed Basic Research Fund of Korea Science and Engineer-ing Foundation.
Publisher Copyright:
© 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 1994
Y1 - 1994
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84961652359&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84961652359
T3 - Proceedings - International Conference on Pattern Recognition
SP - 507
EP - 509
BT - Proceedings of the 12th IAPR International Conference on Pattern Recognition - Conference B
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994
Y2 - 9 October 1994 through 13 October 1994
ER -