ASL Mobile Translator with CNN Algorithm

[{“box”:0,”content”:”

n

Year : November 23, 2023 | Volume : 01 | Issue : 02 | Page : 33-40

n

n

n

n

n

n

By

n

    n t

    [foreach 286]n

    n

    Lekha Achuth, Sasikumar M

  1. [/foreach]

    n

n

n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Associate Professor, Student, Department of Computer Applications, People’s Education Society University, Bengaluru, Department of Computer Applications, People’s Education Society University, Bengaluru, Karnataka, Karnataka, India, India
  2. n[/if 1175][/foreach]

n

n

Abstract

nThere are around 63 million people in India who are deaf and dumb, and it goes all the way up to 300 million across the world. All of them face issues in their day-to-day life as they can only have conversations with gestures. ASL Mobile Translator with CNN Algorithm is an easy-to-use mobile application which uses complex images and video recognizing models built with an open-source tool called OpenCV. It efficiently recognizes gestures being shown to the camera and translates it to voice message which can be understood by a normal person. As being a mobile application it’s portable and can be carried anywhere you go.

n

n

n

Keywords: American sign language, convolutional neural networks, image classification, machine learning, object detection

n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Algorithms Design and Analysis Review(ijadar)]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in International Journal of Algorithms Design and Analysis Review(ijadar)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article: Lekha Achuth, Sasikumar M ASL Mobile Translator with CNN Algorithm ijadar November 23, 2023; 01:33-40

n

How to cite this URL: Lekha Achuth, Sasikumar M ASL Mobile Translator with CNN Algorithm ijadar November 23, 2023 {cited November 23, 2023};01:33-40. Available from: https://journals.stmjournals.com/ijadar/article=November 23, 2023/view=0/

nn


nn[if 992 equals=”Open Access”] Full Text PDF[else] nvar fieldValue = “[user_role]”;nif (fieldValue == ‘indexingbodies’) {n document.write(‘Full Text PDF‘);n }nelse if (fieldValue == ‘administrator’) { document.write(‘Full Text PDF‘); }nelse if (fieldValue == ‘ijadar’) { document.write(‘Full Text PDF‘); }n else { document.write(‘ ‘); }n [/if 992] [if 379 not_equal=””]nn

Browse Figures

n

n

[foreach 379]n

n[/foreach]n

nn

n

n[/if 379]n

n

References

n[if 1104 equals=””]n

1. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770–778).

2. Jenkins, Jon & Rashad, Sheriff, An Innovative Method for Automatic American Sign Language Interpretation using Machine Learning and Leap Motion Controller. IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2021, pp. 633–638 0633-0638. 10.1109/UEMCON53757.2021.9666640

3. Boulares, Mehrez & Jemni, Mohamed, Mobile sign language translation system for deaf community. W4A 2012 – International Cross-Disciplinary Conference on Web Accessibility, 2012 10.1145/2207016.2207049.

4. Bird, Jordan & Ekárt, A. & Faria, Diego, British Sign Language Recognition via Late Fusion of Computer Vision and Leap Motion with Transfer Learning to American Sign Language, Sensors (Basel, Switzerland), 2020, Volume 20, pp. 5151 10.3390/s20185151

5. Murthy GR, Jadon RS. A review of vision based hand gestures recognition. International Journal of Information Technology and Knowledge Management. 2009 Jul;2(2):405–10.

6. Tawalare, Swati & Karale, Nikhil & Pande, Sagar & Khamparia, Aditya. (2022), Identification of Characters (Digits) Through Customized Convolutional Neural Network, Lecture Notes on Data Engineering and Communications Technologies, 2022, Volume 122, pp. 38–47 10.1007/978-981-16-6285-0_38.

7. Srivastava, S., Gangwar, A., Mishra, R., & Singh, S. Sign Language Recognition System Using TensorFlow Object Detection API, 2022, Volume 1, pp. 48 10.1007/978-3-030-96040-7_48.

8. Johnny S, Nirmala SJ. Sign Language Translator Using Machine Learning. SN Computer Science. 2022 Jan;3:1–6.

9. Ahmed Alsaffar, Hai Tao, Mohammed Ahmed Talab, Review of deep convolution neural network in image classification. International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2017, Volume 1, pp. 1-6 10.1109/ICRAMET.2017.8253139.

10. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet. (2015), Going Deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, Volume 2015, pp. 1-9 10.1109/CVPR.2015.7298594.

11. Goyal S, Sharma I, Sharma S. Sign language recognition system for deaf and dumb people. International Journal of Engineering Research Technology. 2013 Apr;2(4).

12. Alex Krizhevsky, llya Sutskever, Geoffrey E. Hinton, (2012). ImageNet Classification with Deep Convolutional Neural Networks. 10.1145/3065386

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
  2. n[/foreach]

n[/if 1104]

nn


nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

Regular Issue Subscription Review Article

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

Volume 01
Issue 02
Received October 27, 2023
Accepted November 2, 2023
Published November 23, 2023

n

n

n

[if 1190 not_equal=””]n

Editor

n

[foreach 1188]n

n[/foreach]

n[/if 1190] [if 1177 not_equal=””]n

Reviewer

n

[foreach 1176]n

n[/foreach]

n[/if 1177]

n

n

n

n function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n n function myfun() {n x = document.getElementById(“editor”);n y = document.getElementById(“down”);n z = document.getElementById(“up”);n if (x.style.display == “none”) {n x.style.display = “block”;n }n else {n x.style.display = “none”;n }n if (y.style.display == “none”) {n y.style.display = “block”;n }n else {n y.style.display = “none”;n }n if (z.style.display == “none”) {n z.style.display = “block”;n }n else {n z.style.display = “none”;n }n }n function myfun2() {n x = document.getElementById(“reviewer”);n y = document.getElementById(“down2”);n z = document.getElementById(“up2”);n if (x.style.display == “none”) {n x.style.display = “block”;n }n else {n x.style.display = “none”;n }n if (y.style.display == “none”) {n y.style.display = “block”;n }n else {n y.style.display = “none”;n }n if (z.style.display == “none”) {n z.style.display = “block”;n }n else {n z.style.display = “none”;n }n }n”}]