SIGN LANGUAGE DETECTION AND GESTURE RECOGNITION
Abstract
Sign language is a very important means of communication for people with hearing difficulties, through which they can express themselves and interact. In our project, we developed a system that quickly understands gestures in sign languages as performed in videos. For this system we have used an advanced object detecting algorithm known as YOLO v5. Our goal is to help the deaf communicate better by recognizing their signs.
We have trained our system to follow different signs in real time video feeds and interpret them into texts. After much testing and fine-tuning, we made sure that our model could perform well even in contrasting situations. More than any other technique, it can accurately comprehend what is being signed. This system has taken major strides towards assisting the deaf through such technologies like computer vision and deep learning.
Our system in the real-time sign language detection has come up with an effective solution to help improve communication and accessibility by people with hearing impairment. Furthermore, it helps easier interaction between deaf individuals and those who can hear properly. Through this project, we anticipate facilitating easy participation in meaningful engagements that will lead to their empowerment in order express themselves better and communicate assertively with others.
References
2. https://www.tensorflow.org/
3. https://en.wikipedia.org/wiki/Convolutional_neural_network
4. https://opencv.org/
5. https://www.sciencedirect.com/science/article/pii/S2667305321000454
6. https://ieeexplore.ieee.org/document/10053506#:~:text=Image%20processing%20makes%20sign%20language,techniques%20on%20image%2C%20if%20necessary.
7. Indu, M., Swetha, N. and Saritha, C., 2023, March. Smart Chatbot for College Information Enquiry Using Deep Neural Network. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)(Vol. 1, pp. 991-994). IEEE.
8. Nikhath, A.K., Rab, M.A., Bharadwaja, N.V., Reddy, L.G., Saicharan, K. and Reddy, C.V.M., 2022, January. An Intelligent College Enquiry Bot using NLP and Deep Learning based techniques. In 2022 International Conference for Advancement in Technology (ICONAT)(pp. 1-6). IEEE.
9. Ali, M.S., Azam, F., Safdar, A. and Anwar, M.W., 2022, November. Intelligent Agents in Educational Institutions: NEdBOT-NLP-based Chatbot for Administrative Support Using DialogFlow. In 2022 IEEE International Conference on Agents (ICA) (pp. 30-35). IEEE.
10. Susanna, M.C.L., Pratyusha, R., Swathi, P., Krishna, P.R. and Pradeep, V.S., 2020. College enquiry chatbot. International Research Journal of Engineering and Technology (IRJET), 7(3), pp.784-788
11. Vijayakumar, R., Bhuvaneshwari, B., Adith, S. and Deepika, M., 2019. AI-based student bot for academic information system using machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology,5(2), pp.590-596.
12. Meshram, S., Naik, N., Megha, V.R., More, T. and Kharche, S., 2021, August. College enquiry chatbot using rasa framework. In 2021 Asian Conference on Innovation in Technology (ASIANCON)(pp.1-8).IEE
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties and that the Article has not been published elsewhere. Author(s) agree to the terms that the IJO Journal will have the full right to remove the published article on any misconduct found in the published article.
