Feature based recognition of handwritten Kannada numerals — A comparative study

Optical Character Recognition (OCR) is one of the important field in image processing and pattern recognition domain. Many practical applications use OCR with high accuracy. The accuracy of the Optical Character Recognition system depends on the quality of the features extracted and the effectiveness of the classifier. This paper explores the effectiveness of feature extraction method like run length count (RLC) and directional chain code for the recognition of handwritten Kannada numerals. In this paper, K-Nearest Neighbour (KNN) and Linear classifiers are used for the classification. The novelty of this approach is to achieve better accuracy and time complexity with few features using simple classifiers. Results show that the directional chain code approach outperforms the RLC approach in terms of recognition accuracy.