ISOLATED AND CONTINUOUS HAND GESTURE RECOGNITION BASED ON DEEP LEARNING: A REVIEW
Getting to know sign language is of great research importance as it affects the lives of deaf and mute people and societies in general. The rapid development of deep learning techniques presents new horizons in sign language recognition because it can give more accurate results and deal with large amounts of data. This paper provides an overview of sign language recognition systems that use deep learning as a basis. A review of recent studies in this field and the division of recognition systems into continuous and isolated and the algorithms used in both methods: Recurrent Neural Network (RNN) based method, Convolutional Neural Network (CNN), and Three-Dimensional Convolutional Neural Network (3D-CNN), in addition to the challenges in systems, Identify the signal, problems, and prospects.
Deep learning; Recognition of sign language; isolated identification, Continuous identification, CNN, RNN, LSTM.