Optimal design of reference models for large-set handwritten character recognition

Seong Whan Lee, Hee Heon Song

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

For the recognition of large-set handwritten characters, classification methods based on pattern matching have been commonly used, and good reference models play an important role in achieving high performance in these methods. Learning Vector Quantization (LVQ) has been intensively studied to generate good reference models in speech recognition since 1986. However, the design of reference models based on LVQ has several drawbacks for the recognition of large-set handwritten characters. In this paper, to cope with these, we propose a new method for the optimal design of reference models using Simulated Annealing combined with an improved LVQ3 for the recognition of large-set handwritten characters. Experimental results reveal that the proposed method is superior to the conventional method based on averaging or other LVQ-based methods.

Original languageEnglish
Pages (from-to)1267-1274
Number of pages8
JournalPattern Recognition
Volume27
Issue number9
DOIs
Publication statusPublished - 1994 Dec 1
Externally publishedYes

Fingerprint

Character recognition
Character sets
Vector quantization
Pattern matching
Simulated annealing
Speech recognition
Optimal design

Keywords

  • Large-set handwritten character recognition
  • Learning vector quantization
  • Optimal design of reference models
  • Simulated annealing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Optimal design of reference models for large-set handwritten character recognition. / Lee, Seong Whan; Song, Hee Heon.

In: Pattern Recognition, Vol. 27, No. 9, 01.12.1994, p. 1267-1274.

Research output: Contribution to journalArticle

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