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Proceedings Paper

Distorted Character Recognition Via An Associative Neural Network
Author(s): Richard A. Messner; Harold H. Szu
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Paper Abstract

The purpose of this paper is two-fold. First, it is intended to provide some preliminary results of a character recognition scheme which has foundations in on-going neural network architecture modeling, and secondly, to apply some of the neural network results in a real application area where thirty years of effort has had little effect on providing the machine an ability to recognize distorted objects within the same object class. It is the author's belief that the time is ripe to start applying in ernest the results of over twenty years of effort in neural modeling to some of the more difficult problems which seem so hard to solve by conventional means. The character recognition scheme proposed utilizes a preprocessing stage which performs a 2-dimensional Walsh transform of an input cartesian image field, then sequency filters this spectrum into three feature bands. Various features are then extracted and organized into three sets of feature vectors. These vector patterns that are stored and recalled associatively. Two possible associative neural memory models are proposed for further investigation. The first being an outer-product linear matrix associative memory with a threshold function controlling the strength of the output pattern (similar to Kohonen's crosscorrelation approach [1]). The second approach is based upon a modified version of Grossberg's neural architecture [2] which provides better self-organizing properties due to its adaptive nature. Preliminary results of the sequency filtering and feature extraction preprocessing stage and discussion about the use of the proposed neural architectures is included.

Paper Details

Date Published: 27 March 1987
PDF: 7 pages
Proc. SPIE 0726, Intelligent Robots and Computer Vision V, (27 March 1987); doi: 10.1117/12.937714
Show Author Affiliations
Richard A. Messner, University of New Hampshire (United States)
Harold H. Szu, Naval Research Laboratory (United States)

Published in SPIE Proceedings Vol. 0726:
Intelligent Robots and Computer Vision V
David P. Casasent, Editor(s)

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