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

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.

@article{Mohanty2011,
  title     = {Performance Comparison of SVM and K-NN for Oriya Character Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {1},
  number    = {1},
  year      = {2011},
  publisher = {The Science and Information Organization},
  note      = {Special Issue on Image Processing and Analysis},
  author    = {Sanghamitra Mohanty and Himadri Nandini Das Bebartta},
  doi       = {10.14569/SpecialIssue.2011.010116},
  url       = {https://doi.org/10.14569/SpecialIssue.2011.010116}
}