AUTHOR=Orovwode Hope , Ibukun Oduntan , Abubakar John Amanesi TITLE=A machine learning-driven web application for sign language learning JOURNAL=Frontiers in Artificial Intelligence VOLUME=7 YEAR=2024 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1297347 DOI=10.3389/frai.2024.1297347 ISSN=2624-8212 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.