Conference Agenda

Communication barriers between deaf, nonverbal individuals and hearing people remain significant across various aspects of daily life. This article introduces an application for translating individual hand signs from the alphabet, based on the Puerto Rican Sign Language, into written text, as well as converting spoken language into text through speech recognition technologies. By integrating both gesture-based and voice-based input, the system promotes inclusive communication, especially in educational settings. Its main objective is to reduce communication barriers by offering an intuitive, real-time platform accessible through a standard webcam and microphone. The sign recognition process is based on the use of Artificial Intelligence techniques. The selected architecture combined the strengths of Multi-Layer Perceptron (MLP) and Multi-Layer Perceptron (MLP), allowing the system to learn both spatial patterns from individual frames and temporal dependencies across gesture sequences. To provide users with an accessible and real-time translation experience, a web application was developed using Django, a Python web framework. The system achieved an accuracy exceeding 84%, demonstrating its capacity to recognize both static and dynamic hand gestures with high reliability.