Published Paper Details:

This paper presents the AI-Enhanced Sign Language Conversion System (AISLCS), an advanced assistive technology platform that leverages Computer Vision (CV), Natural Language Processing (NLP), and adaptive deep learning models to interpret and translate sign language gestures into real-time textual captions with remarkable contextual accuracy. In the modern era, where virtual meetings have become integral to education, healthcare, and professional collaboration, AISLCS distinguishes itself by enabling dynamic, human-like interpretation of highly visual and gesture-based communication. [1] The system is engineered to capture, process, and generate meaningful translations from sign gestures, facilitating automated accessibility for inclusive digital interaction and equitable participation in online environments. AISLCS employs sophisticated AI techniques, including gesture detection (for identifying hand shapes and motion trajectories), temporal modeling (to preserve continuity in dynamic signing), sentiment-aware processing (capturing emotional context), and contextual NLP refinement, which ensures grammatically coherent output throughout a session. [2] By combining machine learning models. Blending state-of-the-art deep learning with rule-based interpretation patterns, AISLCS adapts to diverse user profiles, regional sign variations, and evolving vocabularies, delivering a personalized and scalable communication experience. Its applicability spans education (enabling equal classroom participation), corporate collaboration (inclusive professional meetings), telemedicine (supporting patient-doctor consultations), and consumer-facing accessibility tools This work demonstrates how integrating adaptive AI with modern computer vision expands our potential to bridge the communication gap for the Deaf and hard-of-hearing community, making digital collaboration more accessible, engaging, and equitable than ever before. The project shows how modern AI-driven gesture recognition and NLP-based contextual processing can transform user interaction in virtual platforms through advanced automated sign-to-text dialogue systems.