  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.
