A model-based segmentation framework for computer processing of handwriting

Abstract

Describes a model-based segmentation framework for the partitioning of handwriting (handprinted characters, cursive script, signatures). The model accounts for handwriting generation in terms of response patterns that result from the activation by the central nervous system of curvilinear and angular velocity generators, characterized by log-normal impulse responses. In this context, a handwritten trace can be segmented into a hierarchy of well-defined elements: components, strings and curvilinear and angular strokes. One striking conclusion from this approach is that strokes have to be superimposed to generate a smooth handwritten trace and are thus hidden in the trajectory signal. The segmentation algorithm avoids this problem by using an analysis-by-synthesis technique to segment a specific curve.