Toward data quality analytics in signature verification using a convolutional neural network

Many studies have been conducted on Handwritten Signature Verification. Researchers have taken many different approaches to accurately identify valid signatures from skilled forgeries, which closely resemble the real signature. The purpose of this paper is to suggest a method for validating written signatures on bank checks. This model uses a convolutional neural network (CNN) to analyze pixels from a signature image to recognize abnormalities. We believe the feature extraction capabilities of a CNN can optimize processing time and feature analysis of signature verification. Unique characteristics from signatures can be accurately and rapidly analyzed with multiple layers of receptive fields and hidden layers. Our method was able to correctly detect the validity of the inputted signature approximately 83 percent of the time. We tested our method using the SIGCOMP 2011 dataset. The main contribution of this method is to detect and decrease fraud committed, especially in the banking industry. Future uses of signature verification could include legal documents and the justice system.