@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 = {1579 - 1585}, 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}, }