• Dr.D.Likhith Reddy
• B.Lakshmi
• K.Audi Lakshmi
• G.Bhavya
• G.Harish

Signature verification is a widely used biometric authentication method essential for identity validation in financial, legal, and administrative domains. This paper presents an offline signature verification approach utilizing structural feature extraction, coupled with machine learning models such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). The proposed system focuses on extracting unique structural attributes like curvature, stroke endpoints, and junction points to differentiate between genuine and forged signatures. Experimental results on public datasets and self-generated samples demonstrate the method's efficiency, achieving high accuracy and robustness against signature variability and skilled forgeries.

Biometric authentication, signature verification, structural features, SVM, CNN, forgery detection, HOG.

"Signature verification using structural feature extraction method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.m392-m395, April-2025, Available :http://www.jetir.org/papers/JETIR2504C52.pdf

"Signature verification using structural feature extraction method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppm392-m395, April-2025, Available at : http://www.jetir.org/papers/JETIR2504C52.pdf