International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Sign Bridge: AI-Assisted Sign Language Translation |
|---|---|
| ABSTRACT | For many hearing-impaired individuals, everyday communication often involves additional effort, patience, and reliance on others. Simple activities such as asking for directions, participating in discussions, or expressing personal needs can become challenging when sign language is not understood by the surrounding community. This communication gap not only affects information exchange but also impacts confidence, independence, and social inclusion. Addressing this challenge through technology can significantly improve the quality of life for individuals who depend on sign language as their primary means of communication. Sign Bridge aims to serve as a practical communication companion by transforming visual sign gestures into easily understandable text or speech in real time. Unlike traditional solutions that work only in controlled environments, the proposed system focuses on usability in everyday scenarios. By combining intelligent gesture recognition with language understanding, the system adapts to natural variations in signing styles while maintaining accuracy. Its web-based and modular design allows flexible deployment and future expansion, making it suitable for diverse users and environments. Overall, Sign Bridge represents a step toward more inclusive digital communication, where technology actively supports equality, accessibility, and human connection. Despite rapid advances in digital communication, many existing technologies still overlook the needs of sign language users. Communication often depends on the availability of interpreters, which may not be feasible in spontaneous or everyday situations. This gap highlights the need for intelligent systems that can understand visual language as naturally as spoken or written forms. By focusing on real-time interaction, Sign Bridge aims to make communication more immediate, natural, and accessible for users who rely on sign language. The system is designed with practicality and inclusivity in mind, emphasizing ease of use and adaptability rather than complexity. Its ability to operate in real-world environments allows users to communicate confidently without technical barriers. By reducing communication dependency and encouraging independent interaction, Sign Bridge supports equal participation across social, educational, and professional settings. Ultimately, the proposed approach demonstrates how artificial intelligence can be applied responsibly to address real human challenges, fostering a more inclusive and connected society through accessible communication technology. By enabling instant understanding between sign language users and non-signers, the system helps reduce communication delays and social barriers. This approach highlights the potential of AI-driven solutions to create more empathetic and inclusive human–computer interactions. |
| AUTHOR | AKSHATHA M, AKSHATH M J, D S LAVANYA, DEEPAK M, H S VARUN, MONALISA S Department of Computer Science and Engineering, Maharaja Institute of Technology, Mysore Affiliated to Visvesvaraya Technological University (VTU), Belagavi, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312150 |
| pdf/150_Sign Bridge AI-Assisted Sign Language Translation.pdf | |
| KEYWORDS | |
| References | 1. Sonali M. Antad, Siddhartha Chakrabarty, Sneha Bhat and Somrath Bisen, “Sign Language Translation Across Multiple Languages,” International Journal of Computer Applications, Vol. 176, Issue 25, pp. 1–5, 2020. 2. Kinjal Bhargavkumar Mistree, Devendra Thakor and Brijesh Bhatt, “An Approach Based on Deep Learning for Indian Sign Language Translation,” International Journal of Engineering Research & Technology (IJERT), Vol. 8, Issue 6, pp. 123–128, 2019. 3. Harini R, Janani R, Keerthana S, Madhubala S and Venkatasubramanian S, “Sign Language Translation,” International Journal of Engineering Research & Technology (IJERT), Vol. 10, Issue 3, pp. 456–460, 2021. 4. Ankith Boggaram, Aaptha Boggaram, Ashwin Srinivasa Ramanujan and Bharathi R, “Sign Language Recognition and Translation Systems for Enhanced Communication,” IEEE Access, Vol. 10, pp. 1547–1562, 2022. 5. Shagun Katoch, Varsha Singh and Uma Shanker Tiwary, “Indian Sign Language Recognition System with SURF with SVM and CNN,” Neural Computing and Applications, Springer, Vol. 30, Issue 11, pp. 3676–3685, 2018. |
