SigScatNet: A Siamese + Scattering based Deep Learning Approach for
  Signature Forgery Detection and Similarity Assessment
Anmol Chokshi Vansh Jain Rajas Bhope Sudhir Dhage
The surge in counterfeit signatures has inflicted widespread inconveniences
and formidable challenges for both individuals and organizations. This
groundbreaking research paper introduces SigScatNet, an innovative solution to
combat this issue by harnessing the potential of a Siamese deep learning
network, bolstered by Scattering wavelets, to detect signature forgery and
assess signature similarity. The Siamese Network empowers us to ascertain the
authenticity of signatures through a comprehensive similarity index, enabling
precise validation and comparison. Remarkably, the integration of Scattering
wavelets endows our model with exceptional efficiency, rendering it light
enough to operate seamlessly on cost-effective hardware systems. To validate
the efficacy of our approach, extensive experimentation was conducted on two
open-sourced datasets: the ICDAR SigComp Dutch dataset and the CEDAR dataset.
The experimental results demonstrate the practicality and resounding success of
our proposed SigScatNet, yielding an unparalleled Equal Error Rate of 3.689%
with the ICDAR SigComp Dutch dataset and an astonishing 0.0578% with the CEDAR
dataset. Through the implementation of SigScatNet, our research spearheads a
new state-of-the-art in signature analysis in terms of EER scores and
computational efficiency, offering an advanced and accessible solution for
detecting forgery and quantifying signature similarities. By employing
cutting-edge Siamese deep learning and Scattering wavelets, we provide a robust
framework that paves the way for secure and efficient signature verification
systems.