Unreadable Offline Handwriting Signature Verification Based on Generative Adversarial Network Using Lightweight Deep Learning Architectures

Abstract

Today, it is known that there are great difficulties and problems in signature and signature examinations, which have a very important place in both our private life and business and commercial life. The major issue arises when the manuscript’s signature is so illegible and unclear that it is difficult, if not impossible, to authenticate it with the human eye. Researchers have proposed traditional deep learning techniques to solve or improve this challenge. However, the results are not satisfactory. In this study, a new use of Generative Adversarial Network (GAN) model is proposed as a high-quality data synthesis method to address the unreadable data problem on signature verification. A unique signature verification method based on Lightweight deep learning architecture is also proposed. The suggested data synthesizing approach is evaluated using three frequently used Convolutional Neural Network (CNN) methods: MobileNet, SqueezeNet, and ShuffleNet. In addition, in preprocessing phase, we added three different types of high-intensity noise, including Salt & Pepper (S&P), Gaussian, and Gaussian Blur, to the images to make the signature unreadable. We utilized Indic scripts dataset to train GAN and CNN models in our approach. The great quality of images generated by GAN model, as well as the signature verification of the generated images, point to the suggested model’s strong performance.