Bibtex entry:

@inproceedings{wirn93:Kov,
   key = {wirn93:Kov},
   keywords = {ocr, pattern recognition, classifiers, algorithms,
               neural networks},
   author = {Zs. M. Kovacs-V. and  R. Guerrieri and  G. Baccarani},
   title = {A Hybrid System for Handprinted Character Recognition},
   booktitle = {Proc. of WIRN-93 Vietri - Italy},
   pages = {322--327},
   month = {May},
   year = {1993},
   abstract={
In this paper a hybrid handprinted character recognition system is
presented. Four classifiers based on simple features work in parallel
and their cooperation is used for quality improvement. The four
classifiers are based on two different normalization sequences, on two
different feature extraction methods and on two different
classification techniques. The results of the classifiers are
combined using three different methods. The first one is based on
rules, the second method employes a weighed sum combination, based on
a perceptron layer, while the third technique uses a multilayer
perceptron acting as supervisor, which extracts the overall
information contained in the output of the classifiers. The results
obtained on the NIST Test Data 1 are reported using the upper-case
letters in the NIST Special Database 3 as training set, featuring
4.20%, 3.74%, 3.68% error rate when no rejection is allowed,
respectively for the three methods.
},
}

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