Fuzzified deep learning based forgery detection of signatures in the healthcare mission records

Ishu Priya, Nisha Chaurasia, Ashutosh Kumar Singh, Nakul Mehta, Abhishek Singh Kilak, Ahmed Alkhayyat

Abstract:

In an era subjected to digital solutions, handwritten signatures continue playing a crucial role in identity veriication and
document authentication. These signatures, a form of bio-metric veriication, are unique to every individual, serving as a
primitive method for conirming identity and ensuring security of an individual. Signatures, apart from being a means of
personal authentication, are often considered a cornerstone in the validation of critical documents and processes, especially
within the healthcare sector. In healthcare missions, particularly in the regions that are underdeveloped, hand-written records
persist as the primary mode of documentation. The credibility of these handwritten documents hinges on the authenticity of
the accompanying signatures, making signature veriication a paramount safeguard for the integrity and security of medical
information. Nonetheless, traditional oline methods of signature identiication can be time-consuming and ineicient,
particularly while dealing with a massive volume of documents. This arises the evident need for automated signature
veriication systems. Our research introduces an innovative signature veriication system which synthesizes the strengths
of fuzzy logic and CNN (Convolutional Neural Networks) to deliver precise and eicient signature veriication. Leveraging
the capabilities of Fuzzy Logic for feature representation and CNNs for discriminative learning, our proposed hybrid model
ofers a compelling solution. Through rigorous training, spanning a mere 28 epochs, our hybrid model exhibits remarkable
performance by attaining a training accuracy of 91.29% and a test accuracy of 88.47%, underscoring its robust generalization
capacity. In an era of evolving security requirements and the persistent relevance of handwritten signatures, our research
links the disparity between tradition and modernity.