Accuracy Enhancement in Offline Signature Verification with The Use of Associative Memory

In today’s world every official document need the Hand written signature. To verify that the signature done by the person is accurate and not forged, human brain stores the pattern, data and everything so that they can recognize the pattern or data if again the same thing is shown. But the human mind receives the signal from eye which cannot catch the small variation in the pattern so it becomes a big problem for the legal issues. Associative memory is used for the verification of offline signature is used. In this paper we extend the algorithm for the memory of the system and how it will check the correct signature and forged signature .The 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. These algorithms are trained with the original or genuine signature and tested with a sample of ten very similar-looking forged signatures. The study concludes that AMN detects forgery with accuracy 94.3%, which is comparable to other methods cited in this paper.