Signature Verification Through Simulation

  • K. Thamizhmaran Assistant Professor, Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakkanur, Theni, Tamilnadu, India


Emphasizes the need for an automatic verification system. Verification can be performed either Offline or Online based on the application. Online systems use dynamic information of a signature captured at the time the signature is made. Offline systems work on the scanned image of a signature. We have worked on the Offline Verification of signatures using a set of shape based geometric features. The features that are used Baseline Slant The fact that the signature is widely used as a means of personal verification Angle, Aspect Ratio, Normalized Area, Center of Gravity, number of edge points, number of cross points, and the Slope of the line joining the Centers of Gravity of two halves of a signature image. Before extracting the features, preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. The system is initially trained using a database of signatures obtained from those individuals whose signatures have to be authenticated by the system. For each subject a mean signature is obtained integrating the above features derived from a set of his/her genuine sample signatures. This mean signature acts as the template for verification against a claimed test signature. Euclidian distance in the feature space between the claimed signature and the template serves as a measure of similarity between the two. If this distance is less than a pre-defined threshold (corresponding to minimum acceptable degree of similarity), the test signature is verified to be that of the claimed subject else detected as a forgery. The details of preprocessing as well as the features depicted above are described in the report along with the implementation details and simulation results.

How to cite this article:
Thamizhmaran K. Signature Verification Through Simulation. J Adv Res Electro Engi Tech 2021; 8(1&2): 18-21.



1. Papamarkos N, Baltzakis H. Off-line signature verification using multiple neural network classification structures.
2. Plamondon R, Lorette G. Automatic Signature Verification and Writer Identification The State of the Art, Pattern Recognition. 1989; 22(2): 107-131.
3. Gonzalez RC, Woods E. Digital Image Processing. Addison-Wesley.
4. Dimauro G, Impedovo S, Lucchese MG et al. Recent Advancements in Automatic Signature Verification”,Handwriting Recognition.
5. Baltzakis H, Papamarkos N. A new signature verification technique based on a two-stage neural network classifier. Engineering Application of AI 2001; 14: 95-103.
6. Sabourin R, Plamondon R, Beaumier L. Structural interpretation of handwritten signature images. IJPRAI 1994; 8(3): 709-748.
How to Cite
THAMIZHMARAN, K.. Signature Verification Through Simulation. Journal of Advanced Research in Electronics Engineering and Technology, [S.l.], v. 8, n. 1&2, p. 18-21, june 2021. ISSN 2456-1428. Available at: <>. Date accessed: 24 sep. 2023.