Abstract: Signatures play a vital role in various sectors such as banking, finance, and commerce, serving as unique identifiers for individuals. Nonetheless, they present challenges as even slight similarities between two signatures authored by the same person can exist. To tackle this issue and prevent identity fraud in banks and other organizations, forgery detection systems employ machine learning algorithms and concepts like VGG16. These systems utilize structural parameters and local variations within signatures to accurately match them against a database. Implementing such software ensures secure validation across numerous platforms including loan applications, legal document signings, and other relevant processes.

| DOI: 10.17148/IARJSET.2024.11496