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.
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.