Handwritten character recognition based on the nonlinear PCA neural network

Guang Min Sun; Cheng Zhang; Peng Wang; Chao Deng

Handwritten character recognition based on the nonlinear PCA neural network

Guang Min Sun, Cheng Zhang, Peng Wang, Chao Deng

Research output: Contribution to journal › Article › peer-review

Abstract

Principal component analysis (PCA) has been applied widely in pattern recognition. Based on the nonlinear PCA algorithm and subspace pattern recognition method, a nonlinear PCA neural network model of signal reconstruction has been proposed in this paper. The method has been used in handwritten digits and characters recognition, and a comparison with BP neural network based classifiers has been made. Some satisfactory results have been obtained. The experiment results show that the average correct identification rate of our method is up to 94.74% for the handwritten digits, and 91.03% for the handwritten characters.

Original language	English
Pages (from-to)	915-919
Number of pages	5
Journal	Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology
Volume	33
Issue number	9
State	Published - Sep 2007
Externally published	Yes

Keywords

Character recognition
Neural networks
PCA

Cite this

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title = "Handwritten character recognition based on the nonlinear PCA neural network",

abstract = "Principal component analysis (PCA) has been applied widely in pattern recognition. Based on the nonlinear PCA algorithm and subspace pattern recognition method, a nonlinear PCA neural network model of signal reconstruction has been proposed in this paper. The method has been used in handwritten digits and characters recognition, and a comparison with BP neural network based classifiers has been made. Some satisfactory results have been obtained. The experiment results show that the average correct identification rate of our method is up to 94.74% for the handwritten digits, and 91.03% for the handwritten characters.",

keywords = "Character recognition, Neural networks, PCA",

author = "Sun, {Guang Min} and Cheng Zhang and Peng Wang and Chao Deng",

year = "2007",

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journal = "Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology",

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TY - JOUR

T1 - Handwritten character recognition based on the nonlinear PCA neural network

AU - Sun, Guang Min

AU - Zhang, Cheng

AU - Wang, Peng

AU - Deng, Chao

PY - 2007/9

Y1 - 2007/9

N2 - Principal component analysis (PCA) has been applied widely in pattern recognition. Based on the nonlinear PCA algorithm and subspace pattern recognition method, a nonlinear PCA neural network model of signal reconstruction has been proposed in this paper. The method has been used in handwritten digits and characters recognition, and a comparison with BP neural network based classifiers has been made. Some satisfactory results have been obtained. The experiment results show that the average correct identification rate of our method is up to 94.74% for the handwritten digits, and 91.03% for the handwritten characters.

AB - Principal component analysis (PCA) has been applied widely in pattern recognition. Based on the nonlinear PCA algorithm and subspace pattern recognition method, a nonlinear PCA neural network model of signal reconstruction has been proposed in this paper. The method has been used in handwritten digits and characters recognition, and a comparison with BP neural network based classifiers has been made. Some satisfactory results have been obtained. The experiment results show that the average correct identification rate of our method is up to 94.74% for the handwritten digits, and 91.03% for the handwritten characters.

KW - Character recognition

KW - Neural networks

KW - PCA

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M3 - Article

AN - SCOPUS:35948954009

SN - 0254-0037

VL - 33

SP - 915

EP - 919

JO - Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology

JF - Beijing Gongye Daxue Xuebao / Journal of Beijing University of Technology

IS - 9

ER -

Handwritten character recognition based on the nonlinear PCA neural network

Abstract

Keywords

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