Hossein Gholamalinejad - Department of Computer Engineering, Bozorgmehr University of Qaenat, Qaen, Iran
Marziyeh Felahat - Department of Basic Sciences, Bozorgmehr University of Qaenat, Qaen, Iran

Human hand gestures serve as a non-verbal mode of communication facilitating interaction between humans and computers. Due to their intuitive and natural nature, hand gestures are a key driving force inspiring researchers to enhance human-computer interaction by leveraging the unique capabilities of hands. In this paper, we propose a convolution neural network based on SE-Block for hand gesture recognition on a complex dataset that interprets and responds to hand movements in real time. Our data set includes ۱۶۰۰۰ binary images in ۸ classes. The obtained results show that the accuracy of the proposed network is less than similar deep neural networks. Keywords: Neural network, Hand Gesture recognition, SE-Block.