Bilingual Approach: Leveraging Deep Neural Network Techniques for Handwritten Signature Authentication

In an era defined by technological innovation, the importance of robust authentication mechanisms cannot be overstated, particularly in the realm of security management. Handwritten signature recognition stands as a pivotal component in identity verification processes. Our system harnesses the power of Deep Neural Networks (DNNs) to extract intricate hierarchical features from signature images, effectively capturing both global and local patterns with remarkable precision. To assess and confirm the effectiveness of our algorithm in distinguishing genuine signatures from fraudulent ones, we carefully compile datasets containing both new and existing signatures. Additionally, our work introduces an offline human signature system, underpinned by the sophisticated Histogram Equalization method, further augmenting the robustness of our approach. Our approach, which is based on research utilised in the educational system, is very helpful in protecting and validating academic documents. The addition of bilingual features increases the usefulness of our system by supporting a variety of linguistic signatures that are frequently seen in educational materials. This method guarantees a thorough and flexible solution for handwritten signature authentication in security and educational institutions.