Enhancing Cheque Security: Forgery Detection Via Signature Recognition Using Optical Character Recognition and Deep Learning

Trustworthy verification of bank checks is crucial for modern financial transaction security and the prevention of fraudulent activities. A novel approach to the authentication and categorization of bank check signatures is introduced in this study. It employs a comprehensive architecture that integrates CNN, line sweep algorithms, and optical character recognition (OCR). The proposed method aims to improve the accuracy and efficacy of signature verification processes by harnessing the potential of state-of-the-art technology. First, the system uses optical character recognition to extract text from images of checks; this allows it to recognize crucial components including account data and text-based attributes. The line sweep method to spatial connections within images of checks also helps to emphasize key features like account numbers and signatures. The technique relies heavily on Convolutional Neural Networks. The system is able to differentiate between authentic and false samples based on their distinct patterns and characteristics by training convolutional neural networks (CNNs) using a big collection of signatures. With this wealth of information at its disposal, the system can confidently assess the signatures’ authenticity, resulting in a more robust and accurate verification procedure. Next, they came up with an innovative way to identify signatures and validate bank checks using convolutional neural networks (CNNs), optical character recognition (OCR), and line sweep analysis. With the automation and improvement of verification procedures, monetary transactions will be more trustworthy, less prone to errors, and more secure.