Enabling Communication Strategies: Indian Alpha-Numeric Sign Gesture Recognition

In the realm of effective communication, individuals with hearing impairments face difficult challenges in expressing themselves. There is an immediate and essential need for innovative solutions because of the inherent difficulty. This research strategically tackles this issue by combining natural gestures with advanced technological support, focusing on recognizing Indian Sign Language (ISL) alphabet (A-Z) and digits (1–9) recognition. A comprehensive dataset including 35 Indian sign language alphabets and digits, total of 42,000 images, with each alphabet represented by 1,200 images, is used to ensure a comprehensive coverage for effective communication. Our study introduces a novel approach utilizing Support Vector Machine (SVM) classifiers, K-means Clustering and ORB descriptors for feature detection. Remarkably, the proposed methodology achieves an outstanding accuracy rate of 99.7%, facilitating real-time recognition from live video streams. This research not only sheds light on the challenges faced by individuals with hearing impairments but also actively contributes to the development of inclusive communication platforms, serving as a vital bridge between this community and the general population.