Kernel based principal component for recognizing handwritten numbers

Xiaoxiao Song, Songhua Xu, Willard L. Miranker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We implement kernel-based principal component analysis to recognize handwritten numbers. Then we analyze the relationship of each numeral type's eigenvalue and eigenvector with its recognition rate. We also present a modified algorithm to improve the robustness as well as the efficiency of the recognition method by employing the secondary training and detection methods from the perspective of nature of kernel function. This method can solve the problem of low recognition rate of a small number of scribbled characters at both low time cost and space complexity. Experiments using 1000 to 5000 test samples all show that our method can achieve 97.8% to 99.0% recognition accuracy.

Original languageEnglish (US)
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
StatePublished - Dec 1 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: Jul 18 2010Jul 23 2010

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Country/TerritorySpain
CityBarcelona
Period7/18/107/23/10

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Kernel based principal component for recognizing handwritten numbers'. Together they form a unique fingerprint.

Cite this