Offline Handwritten Signature Verification Using Associative Memory Net
Handwritten signature verification must be accurate due to several legal issues. The verification mandates faster detection as well. Human eye often fails to differentiate accurately a very similar-looking forged signature from its genuine version. The clarification is often a time-taking process. Therefore, such verification, from the computer science perspective, is a bi-objective optimization problem, where cost functions are errors and computational time. This paper studies application of Associative Memory Net (AMN) in correctly detecting forged signatures, fast. Here, the cost functions are handled with detail parametric studies and parallel processing using OpenMP.