Offline tracing and representation of signatures

An approach to the representation of signatures in an offline environment that makes a tracing of a signature in a manner similar to that of a human normally does, and incorporates the dynamic information of the tracing sequence into the representation of the signature, is presented. Tracing involves hierarchical decision-making for stroke identification and ordering based on a set of heuristic rules. These heuristic rules work as operators that transform a two-dimensional (2-D) spatial pattern into a one-dimensional (1-D) temporal pattern, thus making it possible to extract the dynamic features of a signature. Following the stroke sequence of a signature identified by the tracing, a multiresolution critical-point segmentation method is proposed to extract local feature points, at varying degrees of scale and coarseness, for representation. To make the representation translation-, rotation-, and scaling-invariant, a critical-point normalization method is introduced. Experimental results are shown