Handwritten Signature

Handwritten signature analysis focuses on verifying signature authenticity and understanding the underlying writing dynamics, with applications in security and forensics. Current research emphasizes developing robust algorithms for signature verification, often employing machine learning techniques like deep learning and Gaussian processes, alongside novel feature extraction methods such as signature transforms and Radon transforms. These advancements aim to improve accuracy and efficiency in automated signature verification systems, impacting fields ranging from financial transactions to access control, and also contribute to a deeper understanding of human motor control and biometric identification.