Open Access Open Access  Restricted Access Subscription Access

Developing a Machine Learning Model to Translate Sign Language to English Text in Real Time

Deepika S, Kartik Rastogi, Anita Biradar, Jeevika M.Y, Dr. B. Muthu Kumar

Abstract


The hearing-impaired individuals have communication problems with other hearing people. Sign language helps in bridging the communication gap between a hearing person and hearing-impaired individuals. Thus, it is difficult for the people who use sign language to communicate with others who do not understand it without the use of an interpreter. Sign language interpreters are used to translate spoken language to sign language and vice versa. Main purpose is to provide an efficient way to convert sign language into text.

 


Full Text:

PDF

References


Shivashankara, S., & Srinath, S. (2018). American Sign Language recognition system: an optimal approach. International Journal of Image, Graphics and Signal Processing, 11(8), 18.

Trigueiros, P., Ribeiro, F., & Reis, L. P. (2014). Vision-based Portuguese sign language recognition system. In New Perspectives in Information Systems and Technologies, Volume 1 (pp. 605-617). Springer International Publishing.

Rajan, R. G., & Leo, M. J. (2019). A comprehensive analysis on sign language recognition system. International Journal of Recent Technology and Engineering (IJRTE), 7(6).

NB, M. K. (2018). Conversion of sign language into text. International Journal of Applied Engineering Research, 13(9), 7154-7161.

Guo, D., Zhou, W., Li, H., & Wang, M. (2018, April). Hierarchical LSTM for sign language translation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1).

Murali, R. S. L., Ramayya, L. D., & Santosh, V. A. (2020). Sign language recognition system using convolutional neural network and computer vision.

Sivakumar, P., & Kumar, B. M. (2017). A novel method on earlier detection of bone cancer using Markov random field segmentation. International Journal of Biomedical Engineering and Technology, 23(2-4), 148-158.

Kumar, B. M., Ragaventhiran, J., Bhavana, N., Pandian, M. T., Islabudeen, M., & Sampath, A. K. (2022). Extraction and Recovering of Finger Vein Verification Based on Deep Attribute Representation. Malaysian Journal of Computer Science, 29-42.

Muthukumar, B., & Ravi, S. (2012). Adaptive Human Motion Estimation Filter with Integrated Phase Compensator. IJCSNS, 12(7), 63.

Nagarathna, C., Kumar, B. M., Bhavana, N., Manjushree, T. L., & Pattan, D. (2022). Improve the Efficiency of Large RFID Network Using Enhanced Security Data Delivery Model for Machine Learning Based Network Intrusion Detection System–A Survey. International Journal of Human Computations & Intelligence, 1(4), 10-17.


Refbacks

  • There are currently no refbacks.