@Article{cmc.2022.025953,
AUTHOR = {Muhammad Jamil Hussain, Ahmad Shaoor, Suliman A. Alsuhibany, Yazeed Yasin Ghadi, Tamara al Shloul, Ahmad Jalal, Jeongmin Park},
TITLE = {Intelligent Sign Language Recognition System for E-Learning Context},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {72},
YEAR = {2022},
NUMBER = {3},
PAGES = {5327--5343},
URL = {http://www.techscience.com/cmc/v72n3/47480},
ISSN = {1546-2226},
ABSTRACT = {In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines. This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way. The hand gesture recognition based on these features has been implemented for usage in real-time. The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video. The overall algorithm has been tested on two well-known ASL-alphabet (American Sign Language) and ISL-HS (Irish Sign Language) sign language datasets. Different machine learning classifiers including random forest, decision tree, and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been compared. Since the random forest classifier performed better, it has been selected as the base classifier for the proposed system. It showed 96.7% accuracy with ISL-HS and 93.7% accuracy with ASL-alphabet dataset using the extracted features.},
DOI = {10.32604/cmc.2022.025953}
}