Real-time sign language recognition system

One of the more natural ways of interaction between the machine and human is offered by Human gestures which are useful for sign language recognition (SLR). Sign language is the speaking tongue of a segment of people who are known as Deaf people. they are the most people who benefit from the (SLR) by the Human-Computer Interaction (HCI).Most natural people cannot understand these kinds of languages, so Deaf people fail to communicate with them without this helper which achieves by the (HCI). This study introduced a proposed system that offered SLR in real-time for some gestures from the American sign language (ASL), by using one of the most suitable deep learning-based architectures that were called Convolutional neural networks (CNN) and choosing the Squeezenet module After comparing it with a traditional machine learning system that relied on extracting [HOG] features. Squeezenet presented the best result in accuracy reached to (100%) in the off-time testing and about (97.5%)  in the real-time with a competition time of about 3.3sec for capturing the image,  predicting it, and converting it to a text and spoken sentence.The system achieved this result without making any preprocessing for the image, which gave it simplicity and low computing time