This thesis aims to develop a client-server system for translating Italian Sign Language (LIS) into Italian, focusing on improving communication accessibility for individuals who are deaf or hard of hearing. The work was conducted as part of the research project LIS2S, led by Orbyta, which aims to advance sign language translation technologies. The system integrates machine learning, computer vision, and web technologies to bridge the communication gap between LIS users and the broader Italian-speaking community. The proposed system captures video input, which is processed to extract motion data. This data is then used to recognize signs, which are converted into LIS glosses (written representations or transcriptions of the signs in sign language) that capture the meaning of the gestures made.