An efficient transfer learning model for predicting forged (handwritten) signature

Location: Military Institute of Science and Technology, Dhaka, Bangladesh

Timeline: January 2021 - December 2021

• Project Description: The Signature Fraud Detection project employs neural network concepts to combat signature forgery effectively. This system analyses and detects fraudulent signatures with high accuracy by leveraging advanced neural network algorithms. The neural network learns to distinguish between genuine and forged signatures through extensive training on authentic signature samples, enabling swift and precise identification of fraudulent activities. This project showcases the fusion of artificial intelligence and forensic techniques, offering a robust solution for combating signature fraud in various sectors, including finance, legal, and security.

• Project Github Link: SIgnature Fraud Detection

• Publication: An efficient transfer learning model for predicting forged (handwritten) signature