Offline Handwritten Signature Recognition using Convolutional Neural Network Approach
Fazal Noor, Ahmed E. Mohamed, Falah A. S. Ahmed, Salah K. Taha · 2020
Handwritten signature is one of the essential biometric parameters widely used for document validation and verification. Other methods such as fingerprints, iris/retina scanning, face, and voice recognition, although more accurate, need special equipment. The purpose of the research is to demonstrate an appropriate and reliable technology organizations may use to recognize signatures automatically. Convolutional neural networks are trained on preprocessed signature images. The code was developed using MATLAB, and results indicate our method to provide promising results and have contributed by extending the technique to be reliable. The CNN is tested with 4 different datasets with N number of individuals and M number of signatures for each individual and contains signatures that differ from each other in many aspects like the type of signature, its readability, etc. We used our CNN to train and test on all the datasets to observe the performance and make interesting observations of our implementation. The network performed reasonably well on all datasets, which is presented in the results section.