Intelligent Hand Sign Detection for Deaf and Mute People:A Multimodal Approach to Enhancing Communicationthrough AI-Driven Gesture Recognition
Year: 2025
Type: article
Abstract: Intelligent hand sign detection systems offer a transformative solution for enhancing communication between deaf and mute individuals and the broader community. This paper presents a multimodal approach for hand sign detection that integrates advanced AI-driven gesture recognition techniques. By leveraging a combination of computer vision, deep learning, and sensor technologies, the proposed system is capable of accurately recognizing and interpreting a wide range of hand signs. The approach utilizes Convolutional Neural Networks (CNN) for image-based gesture recognition, while also incorporating real-time feedback through audio and visual cues for a more interactive experience. The system is designed to bridge communication barriers, allowing deaf and mute individuals to communicate seamlessly in various real-life scenarios. The integration of multimodal input, including motion sensors and voice synthesis, enhances the robustness of the system, making it adaptable to diverse environments. The results demonstrate the effectiveness of the proposed solution in improving communication accessibility, fostering inclusivity, and empowering individuals with speech and hearing impairments. Keywords: Hand sign detection, Deaf and mute communication, Gesture recognition, Deep learning, Inclusive communication
DOI: 10.55041/ijsrem42545
Authors: Madhav D Ingle1,, Mahesh S. Suryawanshi, Prajakta A. Shinde, Sonali T. Waghmare
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4408527185