AI Powered Sign Language Detection with a Modern Tech Stack

Human-Computer Interaction (HCI) enables effective and natural interaction using hand gestures. Computer vision, deep learning, and image processing technologies are utilized to translate gestures in real-time. The overall goal of this research is to create an efficient system for smooth gesture-based interaction for the hearing and speech disabled. The system utilizes the application of deep learning and convolutional neural networks(CNNs) to provide precise gesture recognition. To enable generalization to users and environments, the model is trained on a robust dataset with a large variety of hand poses and movements. Hand gesture identification and interpretation are done through real-time image capture, feature extraction, and classification. Potential applications of this technology are in robotics, virtual reality, smarts, assistive and intelligent control devices, enhancing accessibility and user interfaces. The system optimizes recognition efficiency and reduces errors by using advanced edge detection and background subtraction methods. Experimental results show high accuracy and real-time responsiveness, validating the effectiveness of the system for practical application. Future research will aim at model generalization, extending gesture repertoires, and bringing together multimodal inputs like speech and facial recognition in order to enrich interaction.