Performance Comparison of SVM and K-NN for Oriya Character Recognition

Abstract

See, stats, and : https : / / www. researchgate. net / publication / 215477612 Performance -NN Oriya Article DOI : 10 . 14569 / SpecialIssue . 2011 . 010116 CITATIONS 6 READS 46 3 , including : Some : Speech Sanghamitra Utkal 55 SEE All . The . Abstract—Image classification is one of the most important branch of Artificial intelligence ; its application seems to be in a promising direction in the development of character recognition in Optical Character Recognition (OCR) . Character recognition (CR) has been extensively studied in the last half century and progressed to the level , sufficient to produce technology driven applications . ow the rapidly growing computational power enables the implementation of the present CR methodologies and also creates an increasing demand on many emerging application domains , which require more advanced methodologies . Researchers for the recognition of Indic Languages and scripts are comparatively less with other languages . There are lots of different machine learning algorithms used for image classification nowadays . In this paper , we discuss the characteristics of some classification methods such as Support Vector Machines (SVM) and K - - earest eighborhood (K -) that have been applied to Oriya characters . We will discuss the performance of each algorithm for character classification based on drawing their learning curve , selecting parameters and comparing their correct rate on different categories of Oriya characters . It has been observed that Support Vector Machines outperforms among both the classifiers .