HandWave: A Convolutional Model for Indian Sign Language Recognition: A Deep Learning Approach

For millions of people worldwide who are deaf or hard of hearing, sign language is their major means of communication. Despite the development of advanced recognition systems for American Sign Language (ASL), Indian Sign Language (ISL) has not received equivalent technological attention. ISL, being one of the most diverse sign languages with regionally varying gestures and complex hand movements, requires tailored technological solutions for effective recognition. This paper presents an in-depth exploration of using Convolutional Neural Networks (CNNs) for the automatic recognition of ISL. The proposed system leverages CNN's powerful feature extraction capabilities to recognize the subtle variations in hand shapes, positions, and movements that characterize ISL. Given that ISL includes a wide variety of gestures with significant spatial and temporal complexities, the CNN model is optimized to process both the spatial features of individual frames and the temporal dependencies between consecutive frames in gesture sequences. We curated a comprehensive dataset of ISL gestures, covering a wide spectrum of signs used across different regions of India. The model is trained on this dataset and evaluated for both static and dynamic gestures, achieving high recognition accuracy in real-time. This system is designed not only to facilitate communication but also to be scalable and adaptable for use in various real-world applications such as education, healthcare, and public services. Our research addresses the current technological gap in ISL recognition by providing a scalable, efficient, and highly accurate system. The implementation of such a system will play a crucial role in enhancing the accessibility of information and communication for India's deaf and hard-of-hearing communities, significantly improving their integration into the broader society.