Study of the neural network application in handwritten-digit recognition

Xuan-Jing Shen

doi:10.1117/12.130874

16 December 1992 Study of the neural network application in handwritten-digit recognition

Xuan-Jing Shen

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130874
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Xuan-Jing Shen "Study of the neural network application in handwritten-digit recognition", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130874

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