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Automatic feature generation for handwritten digit recognition

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2 Author(s)
Gader, P.D. ; Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA ; Khabou, M.A.

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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:18 ,  Issue: 12 )