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A Novel Technique of Sign Language Recognition System using Machine Learning for Differently Abled Person

Rabia Khan*, Rashid Husain**, Rajesh Kumar Tyagi***, Juhi Singh****

* Master of Computer Application, Punjub Technical University, Kapurthala, India.

** Department of Computer Science, Sule Lamido University, Kafin Hausa, Jigawa State, Nigeria.

***-**** Department of Computer Science and Engineering, Amity School of Engineering and Technology, Gurgaon, Haryana, India.

Periodicity:December - February'2022
DOI : https://doi.org/10.26634/jcom.9.4.18594

Abstract

Sign language is a language used by deaf and dumb people to communicate through hand gestures or facial expressions combined with non-manual elements. Various automotive tools and software have been developed by many developers, but they require hardware and an Internet connection, which adds to the cost of the software. In this paper, the presented software captures the hand gesture, and with the help of various machine learning optimization algorithms such as Stochastic Gradient Descent (SGD) and Adam (optimizer), the accuracy will be determined to give predictive value. With the help of computers, it could be a new way of learning for deaf and dumb people. During such a pandemic, various group learning apps and software have been developed for the purpose of conducting online classes, but they are useful for ordinary people. Using this technique will help deaf and dumb people with online learning.

Keywords

Sign-Language, Deaf-Dumb People, Hand Gestures, Machine Learning, Learning Software.

How to Cite this Article?

Khan, R., Husain, R., Tyagi, R. K., and Singh, J. (2022). A Novel Technique of Sign Language Recognition System using Machine Learning for Differently Abled Person. i-manager’s Journal on Computer Science, 9(4), 6-12. https://doi.org/10.26634/jcom.9.4.18594

References

[1]. Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) (pp. 1-6). IEEE. https://doi.org/10.1109/ICEngTechnol.2017.8308186

[2]. Allen, D. M. (1971). Mean square error of prediction as a criterion for selecting variables. Technometrics, 13(3), 469-475.

[3]. Anderson, R., Wiryana, F., Ariesta, M. C., & Kusuma, G. P. (2017). Sign language recognition application systems for deaf-mute people: A review based on input-processoutput. Procedia Computer Science, 116, 441-448. https://doi.org/10.1016/j.procs.2017.10.028

[4]. Bantupalli, K., & Xie, Y. (2018, December). American sign language recognition using deep learning and computer vision. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4896-4899). IEEE. https://doi.org/10.1109/BigData.2018.8622141

[5]. Bragg, D., Koller, O., Bellard, M., Berke, L., Boudreault, P., Braffort, A., & Ringel Morris, M. (2019, October). Sign language recognition, generation, and translation: An interdisciplinary perspective. In The 21^st International ACM SIGACCESS Conference on Computers and Accessibility (pp. 16-31). https://doi.org/10.1145/3308561.3353774

[6]. Cantrell, K., Erenas, M. M., de Orbe-Payá, I., & Capitán-Vallvey, L. F. (2010). Use of the hue parameter of the hue, saturation, value color space as a quantitative analytical parameter for bitonal optical sensors. Analytical Chemistry, 82(2), 531-542.

[7]. Chen, Y., & Zhang, W. (2016, October). Research and implementation of sign language recognition method based on Kinect. In 2016 2^nd IEEE International Conference on Computer and Communications (ICCC) (pp. 1947-1951). IEEE.

[8]. Deora, D., & Bajaj, N. (2012, December). Indian sign language recognition. In 2012 1^st International Conference on Emerging Technology Trends in Electronics, Communication & Networking (pp. 1-5). IEEE. https://doi.org/10.1109/ET2ECN.2012.6470093

[9]. Fadhilah, U., Julia, H., & Saribu, D. (2019). Effectiveness of grammarly application for writing English Abstract. International Journal of Science and Research (IJSR), 8(12), 163–166.

[10]. Ghotkar, A. S., Khatal, R., Khupase, S., Asati, S., & Hadap, M. (2012, January). Hand gesture recognition for indian sign language. In 2012 International Conference on Computer Communication and Informatics (pp. 1-4). IEEE. https://doi.org/10.1109/ICCCI.2012.6158807

[11]. Huang, J., Zhou, W., Li, H., & Li, W. (2015, June). Sign language recognition using 3^rd convolutional neural networks. In 2015 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE. https://doi.org/10.1109/ICME.2015.7177428

[12]. Kumar, S. S., Gatti, R., Kumar, S. K., Nataraja, N., Prasad, R. P., & Sarala, T. (2021, August). Glove Based Deaf-Dumb Sign Language Interpreter. In 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) (pp. 947-950). IEEE. https://doi.org/10.1109/RTEICT52294.2021.9573990

[13]. Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018). Independently recurrent neural network (indrnn): Building a longer and deeper RNN. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5457-5466).

[14]. Mordvintsev, A., & Abid, K. (2014). Opencv-Python Tutorials Documentation. https://opencv24-pythontutorials. readthedocs.io/_/downloads/en/stable/pdf/

[15]. Nikam, A. S., & Ambekar, A. G. (2016, November). Sign language recognition using image based hand gesture recognition techniques. In 2016 Online International Conference on Green Engineering and Technologies (IC-GET) (pp. 1-5). IEEE.

[16]. Papastratis, I., Dimitropoulos, K., Konstantinidis, D., & Daras, P. (2020). Continuous sign language recognition through cross-modal alignment of video and text embeddings in a joint-latent space. IEEE Access, 8, 91170-91180. https://doi.org/10.1109/ACCESS.2020.2993650

[17]. Pigou, L., Dieleman, S., Kindermans, P. J., & Schrauwen, B. (2014, September). Sign language recognition using convolutional neural networks. In European Conference on Computer Vision (pp. 572- 578). Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_40

[18]. Raheja, J. L., Mishra, A., & Chaudhary, A. (2016). Indian sign language recognition using SVM. Pattern Recognition and Image Analysis, 26(2), 434-441. https://doi.org/10.1134/S1054661816020164

[19]. Sabaheta, G. K. (2014). Sign Language Recognition using Neural Networks. TEM Journal, 3(4).

[20]. Tolentino, L. K. S., Juan, R. O. S., Thio-ac, A. C., Pamahoy, M. A. B., Forteza, J. R. R., & Garcia, X. J. O. (2019). Static sign language recognition using deep learning. International Journal of Machine Learning and Computing, 9(6), 821-827. https://doi.org/10.18178/ijmlc.2019.9.6.879

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