Recognition of Hand Written Kannada Numerals Using K-Medoids

Abstract:

Data Clustering is considered as an interesting approach for finding similarities in data and putting similar data into groups. Clustering partitions a data set into several groups such that the similarity within a group is larger than that among groups. This paper explores the cluster-based classification scheme in the context of recognition of handwritten Kannada numerals. In this paper, K-Medoids clustering algorithm is being used for the classification. The features used for the classification are obtained from the directional chain code information of the contour points of the numerals. The proposed algorithm is experimented on nearly 1000 samples of handwritten Kannada numerals and obtained 97% of recognition accuracy.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 5354-5358
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.5354
• Online since: January 2012
• Authors: H.R. Mamatha, K. Srikanta Murthy, A.V. Veeksha, S. Vokuda Priyanka, M. Lakshmi
• Keywords: Clustering, Directional Chain Code, Handwritten Kannada Numeral, K-Medoids, OCR

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