Innovative on-line handwriting identification algorithm based on stroke features

The handwriting analysis is a field of great interest since supports the study of different personal characteristics of the human beings, including identity, character, and neurological disabilities. In particular, the handwriting identification area, which also includes the handwritten signature verification, is a topic continuously investigated since the freehand writing of a manuscript, as well as the appending of a personal signature on a paper document, are still the most widespread ways to certify documents in legal, financial and administrative fields. The rapid diffusion of devices that enable user interaction by means of freehand or capacity pen based writing, and the growing successes obtained in processing the digital handwriting, are allowing us to extend more and more the boundaries of this fascinating area. The automatic handwriting identification is an engaging matter that supports several application contexts including the personal identification. In this paper we present a novel on-line handwriting identification algorithm based on the computation of the static and dynamic features of the strokes composing an handwritten text. Extensive experiments have demonstrated the usefulness and the accuracy of the proposed method. © 2014 Springer International Publishing.The handwriting analysis is a field of great interest since supports the study of different personal characteristics of the human beings, including identity, character, and neurological disabilities. In particular, the handwriting identification area, which also includes the handwritten signature verification, is a topic continuously investigated since the freehand writing of a manuscript, as well as the appending of a personal signature on a paper document, are still the most widespread ways to certify documents in legal, financial and administrative fields. The rapid diffusion of devices that enable user interaction by means of freehand or capacity pen based writing, and the growing successes obtained in processing the digital handwriting, are allowing us to extend more and more the boundaries of this fascinating area. The automatic handwriting identification is an engaging matter that supports several application contexts including the personal identification. In this paper we present a novel on-line handwriting identification algorithm based on the computation of the static and dynamic features of the strokes composing an handwritten text. Extensive experiments have demonstrated the usefulness and the accuracy of the proposed method. © 2014 Springer International Publishing