International Journal of Science, Engineering and TechnologyAuthors: Swastik Manglam, Dr. Poonam Dahiya, Dr. Gaurav Aggarwal
Abstract: Indian Sign Language (ISL) serves as a major communication medium for individuals with hearing and speech impairments; however, its limited accessibility among the general population continues to pose significant challenges. This research presents an AI-based ISL alphabet recognition system designed to accurately identify static hand gestures corresponding to alphabets (A-Z) in real time. This approach integrates CV techniques with DL models to effectively extract hand features and perform robust classification. Unlike conventional systems that rely on strict adherence to predefined gesture patterns, the proposed model emphasizes adaptive recognition by accommodating natural variations in gesture execution across different users and environmental conditions. The system specifically addresses key challenges such as gesture similarity, lighting variations, and differences in hand orientation and appearance. Experimental evaluation demonstrates high recognition accuracy and consistent performance across diverse test scenarios, highlighting the robustness and practical applicability of the approach. Beyond its technical contribution, the system supports educational and assistive applications by enabling intuitive, interactive, and non-memorization-based learning for ISL, particularly benefiting children and beginner users. This work forms a foundational component for the development of a comprehensive ISL translation system and contributes toward advancing inclusive and accessible communication technologies.
DOI: http://doi.org/10.5281/zenodo.20591814
