Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory

TL;DR

This paper presents a novel offline signature identification system that fuses multiple classifiers using Support Vector Machines to improve verification accuracy, tested on a large signature database with promising results.

Contribution

It introduces a fusion approach of multiple classifiers with SVM using statistical learning theory for offline signature identification.

Findings

Achieved high accuracy on a database of 5400 signatures

Effective fusion of classifiers improves verification performance

Demonstrated robustness across multiple individuals

Abstract

This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and the signatures are verified with the help of Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers. SVM is used to fuse matching scores of these matchers. Finally, recognition of query signatures is done by comparing it with all signatures of the database. The proposed system is tested on a signature database contains 5400 offline signatures of 600 individuals and the results are found to be promising.