Abstract: Sign language serves as a fundamental medium of communication for individuals with hearing and speech impairments. However, the lack of widespread understanding among the general population often creates a communication barrier, limiting effective interaction. This research presents the design and development of an intelligent sign language detection system capable of recognizing hand gestures corresponding to alphabets, as well as selected words and sentences, in real time.

The proposed system leverages advanced computer vision techniques and deep learning algorithms to accurately interpret hand gestures captured through a camera interface. Image preprocessing methods are employed to enhance input quality, followed by feature extraction and classification using a trained model. The system is designed to achieve high accuracy and efficiency while maintaining robustness under varying lighting conditions and backgrounds.

Furthermore, the model is trained on a diverse dataset of sign language gestures to ensure reliable performance across different users. The output is translated into readable text, enabling seamless communication between sign language users and non-signers. Experimental results demonstrate that the system performs effectively in recognizing both static and dynamic gestures.

This work aims to bridge the communication gap between the deaf-mute community and the wider society, contributing to inclusivity and accessibility through the application of artificial intelligence and human-computer interaction technologies.

Keywords: Sign Language Detection, Computer Vision, Deep Learning, Gesture Recognition, Human-Computer Interaction, Image Processing, Real-Time Systems, Accessibility, Assistive Technology

Downloads: | DOI: 10.17148/IARJSET.2026.13452