Handwritten Signature Recognition using Machine Learning

The primary goal of this study is to develop an accurate and efficient system capable of automated signature verification. To achieve this, a large dataset of both genuine and forged signatures is collected and preprocessed to create a suitable training dataset. The CNN and MobileNet architectures are then employed to extract essential features from the signature images, enabling the system to differentiate between authentic and fraudulent signatures effectively.

The research investigates various aspects of the proposed approach, including data preprocessing, model architecture selection, hyperparameter tuning, and performance evaluation. A comprehensive evaluation is conducted using well-established metrics such as accuracy, precision, and to assess the system's effectiveness in detecting signature fraud and classifying genuine signatures.

KEYWORDS: deep learning, CNN, Mobilenet, image processing, Handwritten signature, Fraud Detection, Signature Verification