Sign Language Recognition Using Convolutional Neural Networks

Basav Bamrah; Jaspreet Kaur

Basav Bamrah Student, Department Computer Science and Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India.
Jaspreet Kaur Assistant Professor, Department Computer Science and Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, India.

Abstract

Sign language is type of communication through body movements, specifically hands and arms are used instead of spoken communication. It the most prominent way used by deaf and mute community to communicate among their own community and with other people. It is also an important kind of communication between human and computers. The work is done using Convolutional Neural Network (CNN), digital image processing methods. A multi-layered CNN is used to train a model that is able to recognize fingerspelling, that is it is able to recognize alphabets from A-Z. Images are processed using Digital Image Processing and by masking method the data set has been created.

How to cite this article: Bamrah B, Kaur J. Sign Language Recognition Using Convolutional Neural Networks. J Adv Res Electro Engi Tech 2022; 9(1&2): 1-5.

References

1. Suharjito, Gunawan H, Thiracitta N et al. Sign Language recognition using modified convolutional neural network model. Bina Nusantara University, West Jakarta, DKI Jakarta, ID. The 1st INAPR International Conference, 7, Jakarta, Indonesia, 2018; 1.
2. Vamplew PW. Recognition of Sign Language Gestures Using Neural Networks. Neuropsychological Trends 1996; 8.
3. Nikam AS, Ambekar A. Sign language recognition using image based hand gesture recognition techniques. Dept. of Elect. & Telecom, Dwarkadas J. Sanghvi College of engineering., Mumbai, India, Online International Conference on Green Engineering and Technologies (IC-GET). 2016; 1-2.
4. Li W, Pu H. Sign language recognition based on computer vision. School of Software, Hanwei Internet of Things Research School, Zhengzhou University, Zhengzhou, China, IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). 2021; 1.
5. Sahoo AK, Kumar K. Sign language recognition: State of the art. Graphic Era Hill University, Sharda University, Researchgate publication 262187093, 2014; 117.
6. Pathak A, Kumar A, Gupta P et al. Real Time Sign Language Detection. Department of Information Technology, Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India. International Journal for Modern Trends in Science and Technology, 33-35.
7. Đogić S, Karli G. Sign Language Recognition using Neural Networks. International Burch University, Francuske revolucije bb, Ilidža, Sarajevo, B&H. TEM Journal 2014; 3(4): 298.