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A genetic sparse distributed memory approach to the application of handwritten character recognition

Identifieur interne : 002453 ( Main/Exploration ); précédent : 002452; suivant : 002454

A genetic sparse distributed memory approach to the application of handwritten character recognition

Auteurs : Kuo-Chin Fan [Taïwan, République populaire de Chine] ; Yuan-Kai Wang [République populaire de Chine]

Source :

RBID : ISTEX:8297B897686BD8A998AB5CAC62B8FD2B7887B5B6

Descripteurs français

English descriptors

Abstract

Kanerva's Sparse Distributed Memory (SDM) is one of the self-organizing neural networks that mimic closely the psychological behavior of the human brain. In this paper, a Genetic Sparse Distributed Memory (GSDM) model that combines SDM with genetic algorithms is proposed. The proposed GSDM model not only maintains the advantages of both SDM and genetic algorithms, but also has higher memory utilization to improve the recognition rate. Its effective performance is also verified by application to Optical Character Recognition (OCR). Experimental results reveal the feasibility and validity of the proposed model.

Url:
DOI: 10.1016/S0031-3203(97)00017-4


Affiliations:


Links toward previous steps (curation, corpus...)


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