Linear Kernel with Weighted Least Square Regression Co-efficient for SVM Based Tamil Writer Identification
Thendral Tharmalingam1, Vijaya Vijayakumar2

1Thendral Tharmalingam, Research Scholar, Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, India.
2Vijaya Vijayakumar, Associate Professor, Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, India.

Manuscript received on 07 March 2019 | Revised Manuscript received on 14 March 2019 | Manuscript published on 30 July 2019 | PP: 586-588 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1629078219/19©BEIESP | DOI: 10.35940/ijrte.B1629.078219
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Abstract: Tamil writer identification is the task of identifying writer based on their Tamil handwriting. Our earlier work of this research based on SVM implementation with linear, polynomial and RBF kernel showed that linear kernel attains very low accuracy compared to other two kernels. But the observation shows that linear kernel performs faster than the other kernels and also it shows very less computational complexity. Hence, a modified linear kernel is proposed to enrich the performance of the linear kernel in recognizing the Tamil writer. Weighted least square parameter estimation method is used to estimate the weights for the dot products of the linear kernel. SVM implementation with modified linear kernel is carried out on different text images of handwriting at character, word and paragraph levels. Comparing the performance with linear kernel, the modified kernel with weighted least square parameter reported promising results.
Index Terms: Weighted Least Square, Parameter Estimation, Support Vector Machine, Tamil Handwriting, Kernels, Modified Kernel.

Scope of the Article: Regression and Prediction