Signature Verification System: Using Big Transfer (BiT-M-R50x1) for Accurate Authentication

Abstract

In the realm of document security, signature verification stands as a vital pillar for establishing authenticity. This study delves into the utilization of the potent Big Transfer (BiT) BiT-M-R50x1 model for the intricate task of signature validation. This dataset encompasses 2149 signature images sourced from diverse individuals, exhibiting notable fluctuations in writing styles, pen pressures, and signature dimensions. By harnessing the prowess of the pre-trained BiT-M-R50x1 model, renowned for its domaingeneralization capability, we fine-tune it to excel in signature verification. The results of our approach unveil remarkable accomplishments on the dataset, yielding a validation accuracy of 98.60%. The meticulously calibrated BiT-M-R50x1 model adeptly distinguishes between authentic and counterfeit signatures, even when confronted with substantial variation. Through the mechanism of transfer learning, the model captures intrinsic attributes that extrapolate effectively to previously unseen signature specimens. Furthermore, we meticulously assess the model's performance concerning the dataset's distinctive signature idiosyncrasies, scrutinizing its adaptability to diverse styles and dimensions. This experiment underscores the potential of harnessing robust pre-trained models like BiT-M-R50x1 for signature verification undertakings, particularly when grappling with intricate and heterogeneous datasets.