Handwritten digit recognition using statistical and rule-based decision fusion
Date Issued
2002
Author(s)
Gjorgjevikj, Dejan
Chakmakov, Dushan
Radevski, Vladimir
Abstract
In this paper, the cooperation of two feature
families for handwritten digit recognition using
SVM (Support Vector Machine) classifiers will be
examined. We investigate the advantages and
weaknesses of various decision fusion schemes
using statistical and rule-based reasoning. The
obtained results show that it is difficult to exceed
the recognition rate of a single classifier applied
straightforwardly on both feature families as one
set by rule based reasoning applied on the
individual classifier decisions. However, the
rule-based cooperation schemes enable an easy and
efficient implementation of various rejection
criteria. On the other hand, the statistical
cooperation schemes offer better possibility for
fine tuning of the recognition versus the reliability
tradeoff, which leads to recognition systems with
high reliability that also keep high recognition
rates.
families for handwritten digit recognition using
SVM (Support Vector Machine) classifiers will be
examined. We investigate the advantages and
weaknesses of various decision fusion schemes
using statistical and rule-based reasoning. The
obtained results show that it is difficult to exceed
the recognition rate of a single classifier applied
straightforwardly on both feature families as one
set by rule based reasoning applied on the
individual classifier decisions. However, the
rule-based cooperation schemes enable an easy and
efficient implementation of various rejection
criteria. On the other hand, the statistical
cooperation schemes offer better possibility for
fine tuning of the recognition versus the reliability
tradeoff, which leads to recognition systems with
high reliability that also keep high recognition
rates.
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