1. SummaryThis article describes a new technique for authenticating handwritten signatures in offline mode. In this technique, each pixel belonging to the signature is taken into account. From the signature, all edge points/end points are extracted. These edge points/end points are connected to form a closed polygonal shape. From the polygonal shape, several values ​​can be calculated that can serve as structural characteristics: form factor, circularity measure, rectangle measure, minimum bounding rectangle, area, and perimeter. These values ​​combine to create a verification function that can help distinguish between genuine and counterfeit signatures.2. IntroductionRecognition of a person can be done on the basis of behavioral or physical characteristics using automated biometric methods. There are many behavioral attributes which can be voice, iris, fingerprint and facial recognition. Due to increasing acts of fraud related to counterfeits, the need to develop such secure systems to authorize the right person has increased and these systems need to be more sensitive to distinguish between the genuine person and the fake person . Among the different methods of identification, the commonly used method in our society is to identify a person through a handwritten signature, as it is an official/formal means of identifying a person. They are used within the government, for attestation, authenticity of documents, etc. But with his social acceptance, he is demoralized by counterfeiting to make fake transactions. The need is to minimize the threats of forgery, research has been done and is still an interesting area for researchers to minimize the acceptance of signature forgery. Automated verification...... middle of paper ......n Offline Signature Verification Methods,” Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004, pages 161–166.[18] Justino, E., Bortolozzi, F. and Sabourin, R. (2005), “A comparison of SVM and HMM classifiers in offline signature verification”, Pattern Recognition Letters, 6(9):1377-1385.[ 19 ] ¨Ozg¨und¨uz, E., S¸ent¨urk, T. and Karslıgil, M. (2005), “Offline verification and signature recognition by support vector machine”, in European Conference on signal processing.[20] Ma, Z., Zeng, the transition to an adaptive multi-resolution wavelet. Class-One-Network”, LECTURE NOTES IN COMPUTER SCIENCE, 4493: 1077[21] Srihari, S., Srinivasan, H., Chen, S. and Beal, M. (2008), “Machine Learning for Signature Verification”, Intelligence (SCI), 90:387–408.