Automated signature verification has many applications in our daily life like Bank-cheque processing,
document authentication, ATM access etc. Handwritten signatures have proved to be important in
authenticating a person's identity, who is signing the document. In this paper we present an off-line
signature verification and recognition system using the global, directional and grid features of signatures.
Support vector machine (SVM) was used to verify and classify the signatures. As there are unique and
important variations in the feature elements of each signature, so in order to match a particular signature
with the database, the structural parameters of the signatures along with the local variations in the
signature characteristics are used. The artificial neural networks are trained by these characteristic. The
system uses the features extracted from the signatures such as centroid, height – width ratio, total area,
first and second order derivatives, quadrant areas etc. After the verification of the signature the angle
features are used in fuzzy logic based system for forgery detection and the performance is increases
approximately (80%) when using SVM as a classifier