Signature Verification and Fraud Detecting Using Opencv and Machine Learning

In the evolving landscape of financial transactions, ensuring the security and authenticity of signature verification on bank cheques is vital to prevent fraud. This paper presents a survey of techniques used in signature verification, covering traditional image processing methods such as feature extraction, edge detection, and shape analysis, alongside modern deep learning approaches like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models. The survey explores key challenges, including handwriting variations, image noise, and the need for reliable real-world systems. Solutions such as pre-processing techniques, data augmentation, and transfer learning are discussed to improve accuracy. Commonly used datasets for training and evaluating models are reviewed, highlighting their features and limitations. The paper also considers ethical concerns such as fairness, transparency, and the broader implications of deploying such systems in financial institutions. By integrating insights from both traditional and advanced methods, this survey provides a valuable reference for researchers and practitioners aiming to enhance the accuracy, robustness, and reliability of signature verification systems. Strengthening these systems contributes to the overall integrity and trust in digital financial transactions.