Analysis of the transformed contour in the writer identification

Abstract

This work shows an approach for the writer identification using off-line handwritten writing. The contour of handwritten words is calculated. That feature is transformed by a Hidden Markov Model, in order to get a hyperdimensionality of new features. Later, a support vector machine classifier is applied to analyze the grade of discrimination. The approach reaches an accuracy between 98,60% and 100%, according to the type of word. This proposal presents a robust and novel system for the based-writing identification in comparison versus the state-of-the-art; and shows the study of its efficiency according to different off-line handwritten words.