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

Authors:
Y. Le Cun;L. D. Jackel;B. Boser;J. S. Denker;H. P. Graf;I. Guyon;D. Henderson;R. E. Howard;W. Hubbard
Affiliations:
AT&T Bell Labs., Holmdel, NJ;-;-;-;-;-;-;-;-
Venue:
IEEE Communications Magazine
Year:
1989

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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