@article{(Open Science Index):https://publications.waset.org/pdf/10002397,
	  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     = {1572 - 1578},
	  ee        = {https://publications.waset.org/pdf/10002397},
	  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},
	}