Modified K-Means and Symbolic Representation in Kannada Character Recognition
B N Ajay1, C Naveena2
1B N Ajay, Department of Computer Science, Visvesvaraya Technological University, Belagavi, Karnataka, India.
2C Naveena, Department of Computer Science, Visvesvaraya Technological University, Belagavi, Karnataka, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 24 May 2019 | PP: 172-178 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F22290486S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper describes an OCR system for printed text documents in Kannada, a South Indian language. Many commercial OCR systems are now available in the market, but most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of works on Indian language charac-ter recognition especially Kannada. In this work we proposed kannada character recognition system using texture features. Here we fuse the texture features like Local Binary Pattern, and Gray Level Local Texture Pattern using concatenation rule and the texture fea- tures are represented using symbolic representation. Finally, Weighted K-means is explored for the purpose of clustering. This method is simple to implement and realize, also it is computationally efficient.
Keywords: LTP, GLTP, Weighted KNN, Segmentation.
Scope of the Article: Computer Science and Its Applications