Machine Learning -Based Signature Verification System | IJCT Volume 13 – Issue 3 | IJCT-V13I3P73

This project proposed an intelligent Signature Fraud Identification System by utilizing the techniques of machine learning to verify the authenticity of the signature in handwriting automatically. This system combines image preprocessing, feature extraction, and a hybrid learning approach to distinguish with precision between genuine and forged signatures. The model analyzes both structural characteristics and writing behavior patterns and detects even skilled forgeries, which are hard to identify with manual inspection. Experimental evaluation with benchmark signature datasets presented high accuracy with low false acceptance and rejection rates. The hybrid CNN–SVM architecture provided balanced performance by utilizing deep learning for feature learning and traditional classification for stable decision boundaries. The system also proved to be adaptable to various writing styles and scripts, and thus applicable in real-world applications such as banking authentication, cheque verification, document validation, and identity security systems. In sum, the proposed approach offers a speedy, reliable, and automated alternative to manual signature verification. Future enhancements could be done on real-time mobile deployment, integration with multimodal biometrics, and continuous learning for evolving fraud patterns. The developed system, therefore, contributes to improvement in security and reduction in financial frauds related to identity.