Hand Written Signature Verification based on Geometric and Grid Features

The fact that the signature is widely used as a means of personal verification emphasizes the need for an automatic verification system. Verification can be performed either Offline or Online based on the application. Offline systems work on the scanned image of a signature. In this paper an Offline Verification of handwritten signatures which use set of simple shape based geometric features. The features used are Mean, Occupancy Ratio, Normalized Area, Center of Gravity, Pixel density, Standard Deviation and the Density Ratio. Before extracting the features, preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. Features Extracted for whole signature first, then extracted for every part after dividing the signature into four sections. For verification, statistical verification techniques are used (Euclidean Distance, Hellinger Distance and Square Chord Distance). The system is trained on three datasets of signatures. The first and the second datasets have English signatures while the third one is collected from people; it contains Arabic and English signatures. The system has been tested on every dataset. The experimental results show that the Euclidean Distance has the average accuracy of 94.42, the Hellinger Distance has the average accuracy of 95.27 and the Square Chord Distance has the average accuracy of 93.14. That result for whole the image and the following average accuracy for image using grid the Euclidean Distance has the average accuracy of 93.54, the Hellinger Distance has the average accuracy of 95.87, and the Square Chord Distance has the average accuracy of 95.93.