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

Author

Le Cun, Y. ; Jackel, L.D. ; Boser, B. ; Denker, J.S. ; Graf, H.P. ; Guyon, I. ; Henderson, D. ; Howard, R.E. ; Hubbard, W.

Author_Institution

AT&T Bell Labs., Holmdel, NJ, USA

Volume

27

Issue

11

fYear

1989

Firstpage

41

Lastpage

46

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

digital signal processing chips; learning systems; neural nets; optical character recognition; automatic learning; character recognition; digital signal processor; feature extraction; handwritten digit recognition; line thinning; local template matching; neural network chips; pattern recognition; postal zip codes; Digital signal processors; Feature extraction; Handwriting recognition; Neural networks;

fLanguage

English

Journal_Title

Communications Magazine, IEEE

Publisher

ieee

ISSN

0163-6804

Type

jour

DOI

10.1109/35.41400

Filename

41400