A saliency-based segmentation method for online cursive handwriting

We propose a model for the segmentation of cursive handwriting into strokes that has been derived in analogy with those proposed in the literature for early processing tasks in primate visual system. The model allows reformulating the problem of selecting on the ink the points corresponding to perceptually relevant changes of curvature as a preattentive, purely bottom-up visual task, where the conspicuity of curvature changes is measured in terms of their saliency. The modeling of the segmentation as a saliency-driven visual task has lead to a segmentation algorithm whose architecture is biologically-plausible and that does not rely on any parameter other than those that can be directly obtained from the ink. Experimental results show that the performance is very stable and predictable, thus preventing those erratic behaviors of segmentation methods often reported in the literature. They also suggest that the proposed measure of saliency has a direct relation with the dynamics of the handwriting, so as it could be used to capture in a quantitative way some aspects of cursive handwriting intuitively related to the notion of style.