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

Kuo Chin Fan, Yuan Kai Wang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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.

Original languageEnglish
Pages (from-to)2015-2022
Number of pages8
JournalPattern Recognition
Volume30
Issue number12
DOIs
StatePublished - Dec 1997

Keywords

  • Genetic algorithms
  • Neural networks
  • Optical character recognition
  • Sparse distributed memory

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