%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/10002442
	%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 1579 - 1585