Invariant Handwriting Features Useful in Cursive-Script Recognition

Abstract

The within-writer variability of handwriting forms one of the problems in the automatic recognition of cursive script. Variability can be handled by choosing handwriting features based upon the process of handwriting generation or upon computational models. Handwriting patterns are represented by a sequence of motor actions, i.e., “strokes”, which can be identified by invariant segmentation. Each stroke is characterized by features related to motor memory parameters which can be identified by their high signal-to-noise ratios.