Employing Deep Learning to Recognize Real from Fake Urdu Signatures
Abstract
Urdu is one of Pakistan's official languages. It is spoken and understood by about 100 million
people around the world including Pakistan and many other countries where pakistani
communities have settled down. The study of methods identifying text written in Urdu script is
an active research area. An interesting study approach could be to identify signatures that are
written in Urdu Language. Deep learning provides many methods that can be used to address
numerous computer vision problems including image classification and object detection. The
state of the art method in deep learning that provides good results in computer vision is
“Convolutional Neural Networks” that were introduced in 1995. The deep convolutional neural
network consists of multiple convolutional and pooling layers. These layers have the ability to
learn the features of the images automatically which results in better accuracy. The research
proposed here employs deep learning methods to identify urdu signature samples as real or
forged. As there has not been much work in Urdu Script so there was no data available online for
urdu signatures. The data set was created by collecting signature samples from high school
students using an offline method. The model used is a convolutional neural network (CNN) that
is trained and then evaluated using urdu signature images.