Recognition of Online Handwritten Isolated Kannada Characters using PCA and DTW
Keerthi Prasad G1, Vinay Hegde2
1Keerthi Prasad G*, Dept. of IS&E, GMIT, Davanagere, India.
2Vinay Hegde, Dept. of CS&E, RVCE, Bengaluru, India.

Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11107-11111 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7810118419/2019©BEIESP | DOI: 10.35940/ijrte.D7810.118419

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Abstract: Handwriting is a natural means of documentation and communication for several years. Human beings communicating with computers through handwritten input would be the best and easiest way of exchanging the information. It is difficult to input data for computers for Indian language scripts because of their complex typing nature. This paper focuses on exploring performance of Principal Component Analysis (PCA) and Dynamic Time Wrapping (DTW) approaches for recognizing online handwritten isolated Kannada characters. Methodology proposed in this paper is writer independent model which recognizes basic 50 Kannada characters including 16 vowels and 34 consonants. Keywords :
Keywords: DTW, Handwritten character Recognition, Online Handwritten Character Recognition, PCA.
Scope of the Article: Pattern Recognition and Analysis.