%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