Optimal design of reference models using simulated annealing combined with an improved LVQ3

Author

Lee, Seong-Whan ; Song, Hee-Heon

Author_Institution

Dept. of Comput. Sci., Chungbuk Nat. Univ., South Korea

fYear

1993

fDate

20-22 Oct 1993

Firstpage

244

Lastpage

249

Abstract

For the recognition of large-set handwritten characters, classification methods based on pattern matching have been commonly used, and good reference models play a very important role in achieving high performance in these methods. Learning vector quantization (LVQ) has been studied intensively 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. To cope with these, the authors propose a 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 and other LVQ-based methods

Keywords

handwriting recognition; learning (artificial intelligence); neural nets; optical character recognition; simulated annealing; vector quantisation; LVQ-based methods; LVQ3; averaging; classification methods; handwritten character recognition; large-set handwritten characters; learning vector quantization; neural nets; optimal design; pattern matching; reference models; simulated annealing; speech recognition; Character recognition; Computational modeling; Computer science; Handwriting recognition; Ink; Iterative algorithms; Pattern matching; Pattern recognition; Simulated annealing; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on

Conference_Location

Tsukuba Science City

Print_ISBN

0-8186-4960-7

Type

conf

DOI

10.1109/ICDAR.1993.395739

Filename

395739