A machine learning-driven web application for sign language learning

Orovwode, H. E. and Ibukun, Oduntan and Abubakar, John Amanesi (2024) A machine learning-driven web application for sign language learning. Frontiers in Artificial Intelligence. pp. 11-17.

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Abstract

Addressing the increasing demand for accessible sign language learning tools, this paper introduces an innovative Machine Learning-Driven Web Application dedicated to Sign Language Learning. This web application represents a significant advancement in sign language education. Unlike traditional approaches, the application’s unique methodology involves assigning users different words to spell. Users are tasked with signing each letter of the word, earning a point upon correctly signing the entire word. The paper delves into the development, features, and the machine learning framework underlying the application. Developed using HTML, CSS, JavaScript, and Flask, the web application seamlessly accesses the user’s webcam for a live video feed, displaying the model’s predictions on-screen to facilitate interactive practice sessions. The primary aim is to provide a learning platform for those who are not familiar with sign language, offering them the opportunity to acquire this essential skill and fostering inclusivity in the digital age.

Item Type:	Article
Uncontrolled Keywords:	machine learning, sign language recognition, CNN, sign language, American Sign Language (ASL), Python
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Patricia Nwokealisi
Date Deposited:	09 Dec 2024 11:57
Last Modified:	09 Dec 2024 11:57
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/18649

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