Profiling Language Sign Using Deep Learning

Large segments of society place a high value on sign language as a distinct communication language. In computer vision, gesture detection is still a developing phenomenon and a hot topic. Aside from its many applications in immersive game technology and human-computer interaction, gesture recognition has many applications in deaf-mute sign language interpretation. In order to profile language signs, we created a model of image processing that recognizes Arabic hand sign language based on artificial neural networks, starting with the data collection process and continuing with data profiling, then using the deep learning model to train and identify gestures, all the way to the presentation of the results. We offer a method to help with the conversion of sign language into voice and text-based formats. The outcomes we have achieved are crucial, important and significant for our next achievements.