Automatic feature generation for handwritten digit recognition
Gader, P.D.
Khabou, M.A.
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Dec 1996
Volume: 18,
Issue: 12
On page(s): 1256-1261
ISSN: 0162-8828
References Cited: 28
CODEN: ITPIDJ
INSPEC Accession Number: 5475514
Digital Object Identifier: 10.1109/34.546262
Current Version Published: 2002-08-06
Abstract
An automatic feature generation method for handwritten digit
recognition is described. Two different evaluation measures,
orthogonality and information, are used to guide the search for
features. The features are used in a backpropagation trained neural
network. Classification rates compare favorably with results published
in a survey of high-performance handwritten digit recognition systems.
This classifier is combined with several other high performance
classifiers. Recognition rates of around 98% are obtained using two
classifiers on a test set with 1000 digits per class
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