Lightweight Siamese Neural Network for Offline Signature Verification: A Data-Efficient Approach

Signature verification is essential in banking, judicial, and governmental institutions. This study presents an offline signature authentication system based on a lightweight Siamese Neural Network (SNN) with metric learning. Unlike large CNN-based based approaches that rely on large datasets and complex architectures, this method utilizes extensive preprocessing and a compact CNN architecture to reduce computational cost while maintaining competitive accuracy. The images are pre-processed before being fed to the two-headed architecture; the system based on denoised, cropped and centered images achieved accuracy of 80-85% on the CEDAR and BHSig260 datasets, while using approximately 1.93 million parameters, significantly fewer than deep CNN-based approaches like state-of-the-art SigNet (10M+ parameters). The results demonstrate that effective preprocessing and hyperparameter tuning can enable data-efficient signature verification without requiring large-scale datasets.