NDLI: An efficient three-stage classifier for handwritten digit recognition

Content Provider	IEEE Xplore Digital Library
Author	Gorgevik, D. Cakmakov, D.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. & Inf. Technol., Univ. "Sv. Kiril i Metodij", Skopje, Macedonia (Gorgevik, D.; Cakmakov, D.)
Abstract	This work proposes an efficient three-stage classifier for handwritten digit recognition based on NN (neural network) and SVM (support vector machine) classifiers. The classification is performed by 2 NNs and one SVM. The first NN is designed to provide a low misclassification rate using a strong rejection criterion. It is applied on a small set of easy to extract features. Rejected patterns are forwarded to the second NN that uses additional, more complex features, and utilizes a well-balanced rejection criterion. Finally, rejected patterns from the second NN are forwarded to an optimized SVM that considers only the "top k" classes as ranked by the NN. This way a very fast SVM classification is obtained without sacrificing the classifier accuracy. The obtained recognition rate is among the best on the MNIST database and the classification time is much better compared to the single SVM applied on the same feature set.
Starting Page	507
Ending Page	510
File Size	292848
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1333822
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handwriting recognition Neural networks Support vector machines Support vector machine classification Feature extraction Spatial databases Information technology Mathematics Computer science Training data
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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