Mirror image learning for handwritten numeral recognition

Meng Shi, Tetsushi Wakabayashi, Wataru Ohyama, Fumitaka Kimura

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

3 Citations (Scopus)

Abstract

This paper proposes a new corrective learning algorithm and evaluates the performance by handwritten numeral recognition test. The algorithm generates a mirror image of a pattern which belongs to one class of a pair of confusing classes and utilizes it as a learning pattern of the other class. Statistical pattern recognition techniques generally assume that the density function and the parameters of each class are only dependent on the sample of the class. The mirror image learning algorithm enlarges the learning sample of each class by mirror image patterns of other classes and enables us to achieve higher recognition accuracy with small learning sample. Recognition accuracies of the minimum distance classifier and the projection distance method were improved from 93.17% to 98.38% and from 99.11% to 99.37% respectively in the recognition test for handwritten numeral database IPTP CD-ROM1 [1].

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - Second International Workshop, MLDM 2001, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Pages239-248
Number of pages10
ISBN (Print)3540423591, 9783540423591
DOIs
Publication statusPublished - 2001
Externally publishedYes
Event2nd International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001 - Leipzig, Germany
Duration: Jul 25 2001Jul 27 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2123 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001
CountryGermany
CityLeipzig
Period7/25/017/27/01

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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