CNN Approach for Static Hand Gesture Recognition in Indian Sign Language

Indian Sign Language (ISL) plays a crucial role in bridging the communication gap between individuals who are hearing-impaired and the broader society. However, limited research and technological solutions exist for recognising ISL, especially in regional contexts. This paper presents a deep learning-based approach for recognising static hand gestures that represent the ISL alphabet (A–Z). A Convolutional Neural Network (CNN) model is trained on a publicly available dataset containing labelled hand sign images. The system classifies input images into corresponding alphabetic characters with high accuracy, providing a real-time, low-cost, and accessible solution. The aim is to support inclusive human-computer interaction and assistive technology for the hearing-impaired community. The experimental results demonstrate the effectiveness of the proposed model, making it suitable for educational tools, basic communication aids, and future integration into mobile or web applications.

Indian Sign Language, CNN, Deep Learning, Hand Gesture Recognition

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