A Siamese Network-based Approach for Robust Offline Signature Verification Under Data Security

Traditional handcrafted feature-based approaches often fail to generalize well under such constraints. leveraging extensive preprocessing and data augmentation strategies. we created our own dataset having 1000 genuine and 1000 forged signatures by individuals. The findings indicate that Siamese-based metric learning is a promising solution for practical offline signature verification systems operating under constrained data conditions. However, the automatic verification of handwritten signatures, particularly in offline scenarios where only static images are available, still presents significant research challenges.