Authors : Manpreet Kaur Sidhu; Snehal Hon; Sandesh Marathe; Tushar A. Rane

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/vv78sten

Scribd : https://tinyurl.com/rfevhbwx

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1891

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Sign Language has been a crucial means of com- munication for the deaf and mute communities worldwide since ages. In India alone, 1 percent of the population consists of hard of hearing and mute individuals. Hence, to help support these marginalized communities, it is important to make use of techno-logical advancements such as deep learning, computer vision and neural network technologies to create systems and applications that can not only help create sign language recognition software for the deaf community, but also provide means to educate others about sign languages around the world. In this paper, we present a system that utilizes Convolutional Neural Networks to recognize the alphabets A-Z of the Indian Sign Language(ISL) by accepting the real time hand signs performed by the user as input from the users’ camera feed and then displays the recognized alphabet label as output in the form of text and speech. We created a custom Indian sign language dataset for all 26 alphabets for this experimentation. The extraction of key features was performed using CNN, background removal, hand segmentation and thresholding.

Keywords : Convolutional Neural Network(CNN), Indian Sign Language(ISL), Deep Learning, Sign Language Recogni-tion(SLR).

References :

1. Prachi Sharma, Radhey Shyam Anand.“A comprehensive evalua- tion of deep models and optimizers for Indian sign language recognition”. Graphics and Visual Computing 5 (2021) 200032. https://doi.org/10.1016/j.gvc.2021.200032
2. Ruiqi Sun, Qin Zhang, Chuang Luo, Jiamin Guo, Hui Chai. “Human ac- tion recognition using a convolutional neural network based on skeleton heatmaps from two-stage pose estimation” Biomimetic Intelligence and Robotics 2 (2022) 100062. https://doi.org/10.1016/j.birob.2022.100062
3. PRABHAKARA R UYYALA. “SIGN LANGUAGE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS” Journal of In- terdisciplinary Cycle Research Volume XIV, Issue I, January/2022 ISSN NO: 0022-1945.
4. Lionel Pigou(B), Sander Dieleman, Pieter-Jan Kindermans, Benjamin Schrauwen. “Sign Language Recognition Using Convolutional Neural Networks” ELIS, Ghent University, Ghent, Belgium (2015)
5. Ahmed Adel Gomaa Elhagry, Rawan Gla Elrayes. “Egyptian Sign Language Recognition Using CNN and LSTM” Computer Vision and Pattern Recognition(2021)
6. Quiroga, Facundo — Antonio, Ramiro — Ronchetti, Franco — Lan- zarini, Laura Cristina — Rosete, Alejandro “A Study of Convolutional Architectures for Handshape Recognition applied to Sign Language” CACIC 2017
7. Nojood M. Alharthi, Salha M. Alzahrani.“Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition”. Applied Sciences(2023).
8. Xianwei Jiang, Yanqiong Zhang1,Juan Lei and Yudong Zhang. “A Survey on Chinese Sign Language Recognition: From Traditional Meth- ods to Artificial Intelligence” Computer Modeling in Engineering & Sciences Tech Science Press(2024)
9. Kartik Shenoy, Tejas Dastane, Varun Rao, Devendra Vyavaharkar “Real- time Indian Sign Language (ISL) Recognition” 9th ICCCNT 2018, IISC, Bengaluru
10. Sundar B. , Bagyammal T. “American Sign Language Recognition for Alphabets Using MediaPipe and LSTM ” 4th International Confer- ence on Innovative Data Communication Technology and Application. 10.1016/j.procs.2022.12.066
11. Ahmed KASAPBAS Ahmed Eltayeb AHMED ELBUSHRA Omar AL- HARDANEE Arif YILMAZ “DeepASLR: A CNN based human com- puter interface for American Sign Language recognition for hearing- impaired individuals ” Computer Methods and Programs in Biomedicine Update 2 (2022) 100048. https://doi.org/10.1016/j.cmpbup.2021.100048
12. Piyush Kapoor, Hema N2 “Sign Language and Common Gesture Using CNN ”. International Journal of Advanced Trends in Computer Science and Engineering ISSN 2278-3091
13. Karan Bhavsar, Raj Ghatiya, Aarti Gohil, Devanshi Thakkar, Bhumi Shah. .“Sign Language Recognition”. International Journal of Research Publication and Reviews Vol (2) Issue (9) (2021) Page 771-777.
14. Rachana Patil, Vivek Patil, Abhishek Bahuguna, and Mr. Gaurav Datkhile. “Indian Sign Language Recognition using Convolutional Neu- ral Network”. ITM Web of Conferences 40, 03004 (2021) ICACC-2021.
15. I.A. Adeyanju, O.O. Bello b, M.A. Adegboyega. “Machine learning methods for sign language recognition: A critical review and analysis”.
16. Intelligent Systems with Applications 12 (2021) 200056
17. Sai Bharath Padigala, Gogineni Hrushikesh Madhav,Saranu Kishore Kumar, . International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056Dr. Narayanamoorthy M. “VIDEO BASED SIGN LANGUAGE RECOGNITION USING CNN-LSTM”. Interna-tional Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056