Improving Offline Handwritten Digit Recognition Using Concavity-Based Features
Keywords:Complementary features, concavity features, digit recognition, feature extraction, handwritten character recognition, off-line recognition.
AbstractThis paper examines benefits of using concavity-based structural features in recognitionÂ of handwritten digits. An overview of existing concavity features is presentedÂ and a new method is introduced. These features are used as complementary featuresÂ to gradient and chaincode features, both among the best performing featuresÂ in handwritten digit recognition. Two support vector classifiers (SVCs) are chosenÂ for classification task as the top performers in previous works; SVC with radial basisÂ function (RBF) kernel and the SVC with polynomial kernel. For reference, we alsoÂ used the k-nearest neighbor (k-NN) classifier. Results are obtained on MNIST, USPSÂ and DIGITS datasets. We also tested dataset independency of various feature vectorsÂ by combining different datasets. The introduced feature extraction method gives theÂ best results in majority of tests.
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