Share Email Print

Proceedings Paper

Study of the neural network application in handwritten-digit recognition
Author(s): Xuan-Jing Shen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, Hopfield networks, Hamming networks, and neocognitron models and their application in handwritten digit recognition are discussed. The neocognitron model is a multilayer network for a mechanism of visual pattern recognition and self-organized by `learning without a teacher,' and it acquires an ability to recognize stimulus patterns based on the geometrical similarity of their shapes without being affected by their positions and distortions, so it showed higher ability to recognize handwritten digits. We developed a handwritten digit recognition system based on the neocognitron (HDRSBN), and carried on the simulation experiments.

Paper Details

Date Published: 16 December 1992
PDF: 6 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130874
Show Author Affiliations
Xuan-Jing Shen, Jilin Univ. (China)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top