Representation of Ballistic Strokes of Handwriting for Recognition and Verification.

Prabhu Teja S (homepage)

The primary computing device interfaces are moving away from the traditional keyboard and mouse inputs towards touch and stylus based interactions. To improve their effectiveness, the interfaces for such devices should be made robust, efficient, and intuitive. One of the most natural ways of communication for humans has been through handwriting. Handwriting based interfaces are more practical than keyboards, especially for scripts of Indic and Han family, which have a large number of symbols. Pen based computing serves four functionalities: a. pointing input b. handwriting recognition c. direct manipulation d. gesture recognition. The second and fourth problems fall under the broad umbrella of pattern recognition problems. In this thesis we focus on efficient representations for handwriting.

In the the first part of this thesis we propose a representation for online handwriting based on ballistic strokes. We first propose a technique to segment online handwriting into its constituent ballistic strokes based on the curvature profile. We argue that the proposed method of segmentation is more robust to noise, compared to the traditional speed profile minima based segmentation. We, then, propose to represent the segmented strokes as the arc of a circle. This representation if validated by the Sigma-lognormal theory of handwriting generation. These features are encoded using a bag-of-words representation, which we name bag-of-strokes. This representation is shown to achieve state- of-art accuracies on various datasets.

In the second part, we extend this representation to the problem of signature verification. To define a verification system, a similarity metric is to be defined. We propose a metric learning algorithm based on the Support Vector Machine (SVM) hyperplanes learned to separate the training data. This results in a very simple metric learning strategy that capable of being modified to increase the number of users registered by the verification system. We experiment with this technique on the publicly available SVC-2004 database and show that this method results in accuracies applicable to practical scenarios. (more...)


Year of completion:  July 2015
 Advisor :

Anoop M. Namboodiri

Related Publications

  • Prabhu Teja S. and Anoop M Namboodiri - A Ballistic Stroke Representation of Online Handwriting for Recognition Proceedings of the 12th International Conference on Document Analysis and Recognition, 25-28 Aug. 2013, Washington DC, USA. [PDF]