Deep Transfer Learning for Authenticating Handwritten Signatures

Handwritten signature authentication is crucial in sectors like banking, security, and education, where identity verification plays a significant role. Signatures are a unique proof of authorization, making their verification essential to prevent forgery and fraud. This study presents a deep learning based approach for handwritten signature verification using transfer learning with the VGG16 model. The model extracts key distinguishing features from hand written signatures and applies a classification method that compares new signatures against stored references using specific threshold values. A well-curated data set undergoes pre-processing to enhance feature extraction before being utilized for training and evaluation. To improve accuracy and robustness, the model incorporates data augmentation techniques that help mitigate variations in handwriting styles and distortions. Adaptive thresholding ensures flexibility in signature matching, reducing false rejections and improving overall reliability. The experimental results highlight the effectiveness of the proposed model, demonstrating a high level of reliability and precision in distinguishing between genuine and forged signatures. This approach provides a robust solution for safe and accurate signature verification in real-world applications, offering significant improvements over traditional manual verification methods.