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Offline Handwritten Numeral Recognition Using Multiple Features and SVM classifier
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  • Journal title : Journal of IKEEE
  • Volume 19, Issue 4,  2015, pp.526-534
  • Publisher : Institude of Korean Electrical and Electronics Engineers
  • DOI : 10.7471/ikeee.2015.19.4.526
 Title & Authors
Offline Handwritten Numeral Recognition Using Multiple Features and SVM classifier
Kim, Gab-Soon; Park, Joong-Jo;
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In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by local shrinking and expanding operations, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where the concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our scheme is tested by recognition experiments on the handwritten numeral database CENPARMI, where SVM classifier with RBF kernel is used. The experimental results show the usefulness of our scheme and recognition rate of 99.10% is achieved.
Numeral recognition;Directional stroke feature;Gradient feature;Concavity feature;SVM;
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