A Secure Method for Authenticity Verification of Handwritten Signatures Through Digital Image Processing and Artificial Neural Networks

This paper aims to propose a method to do authenticity verification of handwritten signatures based on the use of digital image processing and artificial neural networks techniques through the backpropagation learning algorithm with 500 and 901 approaches, in order to optimize this verification process and act as a decision support tool, in an automated way. The results showed an average percentage error of 20% in the first and of 5.83% in the second, while the performance of a trained professional for that has an average error of 6.67%. Thus, we could observe the efficiency of the proposed method, as well as the difference and evolution of approaches through the relevance of the results.