Off-Line Signature Verification Using Local Patterns

Abstract

(Local Directional Pattern) LDPs are more useful than (Local Binary Pattern) LBPs for automatic verification of static signatures. a number of features have been combined with MDF, to capture and investigated various structural and geometric properties of the signatures to perform verification or identification of a signature, several steps must be performed.

Existing System

Our existing system handwritten character recognition using Modified Direction Feature (MDF), it is nothing but systems which recognize a hand written character Modified Direction Feature (MDF) generated encouraging results, reaching an accuracy of 81.58%. In this system each and every hand written character of a separate person is scanned and stored in database the scanned images are verified using MDF.

Proposed System

Our proposed system is Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classification in which we are using MDF with signature images. Specifically, a number of features have been combined with MDF to capture and investigated various structural and geometric properties of the signatures to perform verification or identification of a signature, several steps must be performed. After preprocessing all signatures from the database by converting them to portable bitmap (PBM) format, their boundaries are extracted to facilitate the extraction of features using MDF .Verification experiments are performed with classifiers we are using Radial Basis Function (RBF) which is a classifier which gives an accuracy level of 91.21%.

Architecture