Davood Keykhosravi - Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
Seyed Naser Razavi - Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
Kambiz Majidzadeh - Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.
Amin Babazadeh sangar - Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.

Signature identification plays an important role in many areas such as banking, administrative and judicial systems. For this purpose, in this paper, an automatic intelligent framework is developed by combining a deep pre-trained network with a recurrent neural network. The results of the proposed model were evaluated on several valid datasets and collected datasets. Since there was no suitable Persian signature dataset, we collected a Persian signature dataset based on US ASTM guidelines and standards, which can be very effective and profound for deep approaches. Due to the very promising results of the proposed model in comparison with recent studies and conventional methods, to evaluate the resistance of the proposed model to different noises, we added Gaussian Noise, Salt and Pepper Noise, Speckle Noise, and Local var Noise in different SNRs to the raw data. The results show that the proposed model can still be resistant to a wide range of SNRs; So at ۱۵ dB, the accuracy of the proposed method is still above ۹۰%.

Automatic Identification of the Writer of the Signature, Pre-trained Network, Feature Learning, convolutional neural network (CNN)