Real-Time Sign Language Chat Application with Speech Output

Abstract

The proliferation of digital communication technologies has dramatically transformed human interaction, yet individuals with hearing and speech impairments continue to face significant barriers in mainstream communication systems. Sign language remains the primary mode of communication for the deaf and hard-of-hearing community; however, the vast majority of people outside this community are unable to comprehend it. This communication gap necessitates the development of intelligent, technology-driven bridge systems that can translate sign language into readable and audible formats in real time. Traditional solutions such as human interpreters are expensive, logistically complex, and unavailable in many situations. Recent advances in artificial intelligence, computer vision, and real-time web technologies have opened new possibilities for automated sign language translation. Technologies such as Google’s MediaPipe hand landmark detection framework, WebSocketbased real-time communication, and neural text-to-speech synthesis have matured to the point where accurate, low-latency sign language recognition and translation is practically achievable in browser-based applications. However, many existing automated translation systems suffer from critical limitations: they require specialized, expensive hardware such as data gloves or motion capture suits, are designed as standalone offline applications rather than integrated communication platforms, support only a limited vocabulary, and lack real-time performance necessary for fluid conversation. This project presents the design and implementation of a RealTime Sign Language Chat Application with Speech Output—a full-stack AI-powered system that enables seamless communication using hand gesture recognition. The gesture recognition engine is built using MediaPipe’s hand landmark detection framework, which tracks 21 key points on the human hand in real time through a webcam feed. A rule-based classifier processes the geometric relationships between these landmarks to accurately identify 55 distinct gesture categories: 26 American Sign Language (ASL) alphabetic letters, 10 numeric gestures, and 19 special phrase gestures including greetings, expressions, and commonly used functional words.