Analysis of errors of handwritten digits made by a multitude of classifiers

Authors:
Ching Y. Suen;Jinna Tan
Affiliations:
Centre for Pattern Recognition and Machine Intelligence, Concordia University, CENPARMI, Suite GM 606, 1455 de Maisonneuve Blvd. West, Montréal, Québec, Canada H3G 1M8;Centre for Pattern Recognition and Machine Intelligence, Concordia University, CENPARMI, Suite GM 606, 1455 de Maisonneuve Blvd. West, Montréal, Québec, Canada H3G 1M8
Venue:
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Year:
2005

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Abstract

In this paper we describe an in-depth study on some data misclassified by a collection of classifiers produced by different authors. First of all, we divide the errors into three categories based on their quality and analyze their distributions according to category. Common errors made by three or more classifiers out of five have been identified and analyzed to deduce the reasons of misclassification. Finally, based on systematic analyses, two possible solutions to reduce errors and improve system reliability are proposed: (a) a verification module, and (b) combination of complementary multiple classifiers.