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Improving Offline Handwritten Digit Recognition Using Concavity-based Features (CROSBI ID 188269)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Karić, Miran ; Martinović, Goran Improving Offline Handwritten Digit Recognition Using Concavity-based Features // International Journal of Computers Communications & Control, 8 (2013), 2; 206-219

Podaci o odgovornosti

Karić, Miran ; Martinović, Goran

engleski

Improving Offline Handwritten Digit Recognition Using Concavity-based Features

This paper examines benefits of using concavity-based structural features in recognition of handwritten digits. An overview of existing concavity features is presented and a new method is introduced. These features are used as complementary features to gradient and chaincode features, both among the best performing features in handwritten digit recognition. Two support vector classifiers (SVCs) are chosen for classification task as the top performers in previous works ; SVC with radial basis function (RBF) kernel and the SVC with polynomial kernel. For reference, we also used the k-nearest neighbor (k-NN) classifier. Results are obtained on MNIST, USPS and DIGITS datasets. We also tested dataset independency of various feature vectors by combining different datasets. The introduced feature extraction method gives the best results in majority of tests.

complementary features; concavity features; digit recognition; feature extraction; handwritten character recognition; off-line recognition

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Podaci o izdanju

8 (2)

2013.

206-219

objavljeno

1841-9836

Povezanost rada

Računarstvo

Poveznice
Indeksiranost