Deep Learning-Based Signature Authentication Using Siamese CNN

This project introduces an efficient approach for real-time signature verification using a Siamese Convolutional Neural Network (CNN) combined with threshold optimization techniques. Our model is designed to automate the process of verifying handwritten signatures, offering a secure and reliable solution for applications in sectors like banking, legal documentation, and digital transactions. By utilizing Siamese CNN architecture, the model learns unique features of genuine signatures, enabling it to accurately distinguish them from forgeries. The verification process begins by training the Siamese CNN on a labeled dataset of genuine and forged signature pairs. Each signature pair is processed through identical CNN branches to extract feature embeddings, and the Euclidean distance between these embeddings is calculated to measure similarity. An optimized threshold is applied to determine whether the signatures match, allowing for effective real-time classification. This approach reduces the need for manual verification and provides a scalable solution for high-volume applications, enhancing security and user experience. While the model achieves promising accuracy in distinguishing genuine and forged signatures, future research could explore additional datasets and fine-tuning methods to further improve robustness and adaptability across diverse signature styles.