Conversational Algerian Sign Language Recognition Using the Deep Learning Approach

Continuous Sign Language Recognition (CSLR) is the process of understanding the combination of hand gestures, facial expressions and body pose in real-time, in conjunction with the grammatical and vocabulary structural elements that allow cross-cultural communication with people who have difficulty hearing and/or communicating verbally. This study aims to develop an automatic system capable of detecting the region of interest on the signer's body and classifying each sign into its appropriate category, which will be combined with the natural language (NL). To achieve these objectives, a novel approach is proposed with four main contributions, which can be summarized as the independence of the signer's position in the frame, the collection of an Algerian Sign Language dataset containing 46 dynamic signs (2083 videos), achieving 97.75% accuracy for dynamic sign recognition and 97.37% for static sign recognition and an NLP model to predict the next words in the sentence, thus speeding up communication.