Automatic Feature Generation for Handwritten Digit Recognition

Authors:
Paul D. Gader;Mohamed Ali Khabou
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1996

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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 1,000 digits per class.