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Handwritten digit classification

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  • Andrea Giuliodori

    ()

  • Rosa Lillo

    ()

  • Daniel Peña

    ()

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    Abstract

    Pattern recognition is one of the major challenges in statistics framework. Its goal is the feature extraction to classify the patterns into categories. A well-known example in this field is the handwritten digit recognition where digits have to be assigned into one of the 10 classes using some classification method. Our purpose is to present alternative classification methods based on statistical techniques. We show a comparison between a multivariate and a probabilistic approach, concluding that both methods provide similar results in terms of test-error rate. Experiments are performed on the known MNIST and USPS databases in binary-level image. Then, as an additional contribution we introduce a novel method to binarize images, based on statistical concepts associated to the written trace of the digit

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    File URL: http://e-archivo.uc3m.es/bitstream/10016/11641/1/ws111712.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws111712.

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    Date of creation: Jun 2011
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    Handle: RePEc:cte:wsrepe:ws111712

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    Keywords: Digit; Classification; Images;

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    1. Klaus Nordhausen, 2009. "The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman," International Statistical Review, International Statistical Institute, vol. 77(3), pages 482-482, December.
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