@article{(Open Science Index):https://publications.waset.org/pdf/10002442,
	  title     = {Prediction of Writer Using Tamil Handwritten Document Image Based on Pooled Features},
	  author    = {T. Thendral and  M. S. Vijaya and  S. Karpagavalli},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {6},
	  year      = {2015},
	  pages     = {1586 - 1592},
	  ee        = {https://publications.waset.org/pdf/10002442},
	  url   	= {https://publications.waset.org/vol/102},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 102, 2015},