%0 Journal Article
	%A T. Thendral and  M. S. Vijaya and  S. Karpagavalli
	%D 2015
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 102, 2015
	%T Prediction of Writer Using Tamil Handwritten Document Image Based on Pooled Features
	%U https://publications.waset.org/pdf/10002397
	%V 102
	%X Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.

	%P 1572 - 1578