Toward affine recognition of handwritten mathematical characters

We address the problem of handwritten symbol classification in the presence of distortions modeled by affine transformations. We consider shear, rotation, scaling and translation, since these types of transformations occur most often in practice, and focus most on shear within this framework. We present a distance-based classification method, in which feature vectors are constructed from Legendre-Sobolev expansions of the coordinate functions and of the affine integral invariants of the curves given by the symbol's ink strokes. We analyze different size normalization methods and conclude that integral invariants provide the most robust norm. Finally, we propose a new parameterization, a combination of arc length and time, insensitive to variations in curve tracing speed and affine distortion.

• Type: Conference
• Year: 2010
• Where: DAS
• Authors: Oleg Golubitsky, Vadim Mazalov, Stephen M. Watt
• Keywords: Affine Integral Invariants, Affine Transformations, DAS 2010, Document Analysis, Integral Invariants