Hand Written Signature Verification based on Geometric and Grid Features - Kareem Abd, Baraa; A.Z. Abdullah, Nada; Khaled Abood, Qaswaa (Iraqi Journal of Science, 2023)
The fact that the signature is widely used as a means of personal verificationemphasizes the need for an automatic verification system. Verification can beperformed either Offline or Online based on the application. Offline systems work onthe scanned image of a signature. In this paper an Offline Verification of handwrittensignatures which use set of simple shape based geometric features. The features usedare 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 toremove any spurious noise present. Features Extracted for whole signature first, thenextracted for every part after dividing the signature into four sections. For verification,statistical verification techniques are used (Euclidean Distance, Hellinger Distanceand 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 iscollected from people; it contains Arabic and English signatures. The system has beentested on every dataset. The experimental results show that the Euclidean Distancehas the average accuracy of 94.42, the Hellinger Distance has the average accuracy of95.27 and the Square Chord Distance has the average accuracy of 93.14. That resultfor whole the image and the following average accuracy for image using grid theEuclidean Distance has the average accuracy of 93.54, the Hellinger Distance has theaverage accuracy of 95.87, and the Square Chord Distance has the average accuracyof 95.93.