Slope histogram detection of forged handwritten signatures

A new approach to the problem of handwritten signature verification is presented. This method exploits the regularity of length and curvature of a signature. Overall signature content at various angles is evaluated to form a slope histogram. Histograms are then passed to a classifier constructed from 10 valid signatures. Performance of the classifier on a data pool of 1000 valid and casually forged signatures is evaluated. In particular the equal error rate of this approach is shown to average 7 across 9 different subjects. Increases in the classifier error rates are noted when the forger is allowed some a priori knowledge of the target signature.