Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 278-281.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Handwritten Numeral Recognition Algorithm Based on Similar Principal Component Analysis

HAN Xu, LIU Qiang, XU Jin, CHEN Hai-yun   

  1. School of Electrical Information Engineering,Southwest Petroleum University,Nanchong,Sichuan 637001,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: Principal component analysis (PCA) is one of the most important data reduction algorithms,there is much-maligned views in the process of handling data.A novel improved similar principal component analysis (SPCA) algorithm which is based on principal component analysis (PCA) algorithm was proposed in this paper.This algorithm can keep some detail information in the process.Taking the MNIST handwritten numeral database as an example, the near feature vector is chosen in original vectors to get the groups of non-orthogonal feature vectors.Then,the vectors of trai-ning library is compared with the vectors of testing library,and the recognition rate is calculated.Recognition results indicate that the algorithm can make high identification of the testing samples through a small number of training samples.

Key words: Handwritten numeral recognition, Near feature vector, Non-orthogonal feature vectors, Principal component analysis(PCA)

CLC Number: 

  • TP391.41
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