The evolution of emerging technologies in recent years has opened up new perspectives for automatic sign language recognition. This offers useful tools to support the inclusion of the deaf community and to improve communication between deaf and hearing people. Artificial intelligence systems must carefully interpret a wide range of spatial configurations and movements to convey the correct meanings of sign language, which is based on precise body and hand poses and movements. My thesis focuses on the development of an accessible, cost-effective and real-time sign recognition platform with the aim of promoting inclusive communication in everyday contexts. The developed platform uses a two-stage approach to transform text into recognisable signs and to interpret movements in real time.