R, Chandrasekaran and S, Vijayaraj and M, Balakumaran and C, Arul Stephen and D, Abhijjit and K, Soundarya M. (2025) Interactive Sign Language Learning System-A Comprehensive Application for Sign Language Alphabet and Word Recognition. In: 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), Bhimdatta, Nepal.
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The Interactive Sign Language Learning System is a ground breaking application designed to provide a comprehensive and interactive learning experience for sign language alphabet and word recognition. Built on advanced technologies, this system aims to bridge the gap between individuals with hearing impairments and non-sign language users, enabling effective communication and inclusion. The application offers a user-friendly interface, where learners can easily navigate through various sign language alphabets and individual words, receiving real-time visual feedback and guidance. With a vast library of sign language gestures and clear video demonstrations, learners can practice their signing skills with confidence and accuracy. The system utilizes computer vision and machine learning algorithms to recognize and interpret users' sign language gestures, providing immediate feedback to ensure proper hand movements and positioning. Additionally, the system offers interactive exercises and quizzes, allowing learners to test their knowledge and track their progress over time. The application also incorporates gaming elements, making the learning process engaging and enjoyable. Whether used by individuals wanting to learn sign language or organizations looking to enhance accessibility, this comprehensive learning system is an invaluable tool that empowers users to communicate effectively and inclusively with the deaf community.
Item Type: | Conference or Workshop Item (Paper) |
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Subjects: | Electronics and Communication Engineering > Digital Signal Processing |
Domains: | Electronics and Communication Engineering |
Depositing User: | Mr Tech Mosys |
Date Deposited: | 21 Aug 2025 07:04 |
Last Modified: | 21 Aug 2025 07:04 |
URI: | https://ir.vistas.ac.in/id/eprint/10201 |