Handwritten digit classification

Andrea Giuliodori
Authors registered in the RePEc Author Service: Daniel Peña

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

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

Keywords: Digit; Classification; Images (search for similar items in EconPapers)
Date: 2011-06
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://e-archivo.uc3m.es/rest/api/core/bitstreams ... f1e4161bd6ba/content (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws111712

Access Statistics for this paper

More papers in DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
Bibliographic data for series maintained by Ana Poveda ().