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|Title: ||Handwritten digit classification|
|Author(s): ||Giuliodori, Andrea|
Lillo, Rosa E.
|Publisher: ||Universidad Carlos III de Madrid. Departamento de Estadística|
|Issued date: ||Jun-2011|
|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|
|Serie / Nº.: ||UC3M Working papers. Statistics and Econometrics|
|Appears in Collections:||DES - Working Papers. Statistics and Econometrics. WS|
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