This study presents a prototype system to translate sign language into text, enhancing digital inclusion for individuals with hearing and speech disabilities. The key innovation lies in using a gesture recognition algorithm that analyzes finger positions through the Euclidean distance method. This approach allows the system to accurately interpret and transcribe hand gestures in real-time, capturing movements via a camera and processing them to identify key points on the fingers. The algorithm accurately determines the intended sign, even under varying lighting conditions and hand orientations, by calculating the distances between these points. The Extreme Programming (XP) methodology guided the development, focusing on iterative improvements, user feedback, and adaptability. The project involved multiple iterations to refine the algorithm and user interface, ensuring the system met the specific needs of the deaf community. Extensive testing validated the system's performance, producing a robust real-time sign language translation prototype. The project envisions expanding the system’s capabilities to include advanced features such as sign language dictation in applications like word processors and interaction with smart devices like Alexa using sign language gestures. These future developments aim to enhance the autonomy of the deaf community further, integrating them more fully into the digital world.