Bridging The Communication Gap: Ai-Powered Dual-Mode Isl Gesture Recognition

Indian Sign Language (ISL) communication faces challenges due to the absence of efficient translation systems, limiting accessibility for the hearing and speech impaired. An advanced dual-mode gesture recognition system can bridge this gap. Existing ISL translation systems lack real-time adaptability and struggle with complex gestures, reducing accuracy and usability. study aims to develop an AI-powered dual-mode gesture recognition system that integrates vision-based and sensor-based techniques to enhance ISL translation accuracy and efficiency. A hybrid approach combining deep learning-based image processing for hand gesture recognition and wearable sensors for motion tracking is implemented. Data is collected from diverse ISL signers and processed using neural networks. proposed system demonstrated improved recognition accuracy, faster processing time, and better adaptability to varying lighting conditions and signer variations compared to traditional methods. research contributes to developing a more inclusive and efficient ISL translation system, enhancing communication accessibility. Future advancements can focus on real-time implementation and multilingual sign language support.