Handwritten digit recognition: applications of neural network chips and automatic learning
Yann A. Le Cun, L. D. Jackel, Bernhard E. Boser, John S. Denker, Hans Peter Graf, Isabelle Guyon, D. Henderson, Richard E. Howard, W. Hubbard · IEEE Communications Magazine · 1989
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.>