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Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

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Handwritten alphanumeric character recognition by the neocognitron.

K Fukushima¹, N Wake

¹Dept. of Biophys. Eng., Osaka Univ.

IEEE Transactions on Neural Networks

|January 1, 1991

Summary

This study discusses a neocognitron-based system for recognizing deformed handwritten characters. Effective training pattern selection is crucial for achieving deformation-invariant pattern recognition performance.

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Area of Science:

Artificial Intelligence
Computer Vision
Machine Learning

Background:

The neocognitron, developed by Fukushima in 1980, is a neural network model designed for deformation-invariant visual pattern recognition.
Handwritten character recognition remains a challenging task due to variations in writing styles and deformations.

Purpose of the Study:

To discuss a pattern recognition system based on the neocognitron mechanism.
To investigate techniques for selecting training patterns to improve deformation-invariant recognition of handwritten characters.

Main Methods:

The study discusses a neocognitron-based system trained to recognize 35 handwritten alphanumeric characters.
The system's performance in recognizing deformed characters is evaluated based on the training pattern set.

Main Results:

The ability of the system to correctly recognize deformed characters is highly dependent on the selection of the training pattern set.
The study suggests techniques for selecting training patterns to enhance deformation-invariant recognition.

Conclusions:

The choice of training patterns significantly impacts the deformation-invariant recognition capabilities of neocognitron-based systems.
Specific techniques for training pattern selection are proposed to improve the recognition of a large number of deformed characters.