Handwritten digit recognition: applications of neural network chips and automatic learning

1 November 1989

journal article
Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Magazine

Vol. 27 (11) , 41-46
https://doi.org/10.1109/35.41400

Abstract

Two novel methods for achieving handwritten digit recognition are described. The first method is based on a neural network chip that performs line thinning and feature extraction using local template matching. The second method is implemented on a digital signal processor and makes extensive use of constrained automatic learning. Experimental results obtained using isolated handwritten digits taken from postal zip codes, a rather difficult data set, are reported and discussed.

Keywords

This publication has 5 references indexed in Scilit:

A CMOS associative memory chip based on neural networks
Published by Institute of Electrical and Electronics Engineers (IEEE) ,1987
SPTA: A proposed algorithm for thinning binary patterns
IEEE Transactions on Systems, Man, and Cybernetics, 1984
Some Studies in the Interactive Design of Character Recognition Systems
IEEE Transactions on Computers, 1971
Moment Normalization of Handprinted Characters
IBM Journal of Research and Development, 1970
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
The Journal of Physiology, 1962