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



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.< >


Yann Le Cun
Lionel D Jackel
Isabelle Guyon
John S Denker
Hans Peter Graf
Isabelle Guyon
Don Henderson
Richard E Howard
William Hubbard