@inproceedings{e14157bee223467a8853cc83e2d4a4fe,
title = "Arabic Sign Language Recognition Using Deep Machine Learning",
abstract = "In this work, we present an effective method for automatic Arabic Sign Language recognition that uses a Convolutional Neural Network (CNN) for feature extraction and a Long Short-Term Memory (LSTM) for classification. AlexNet, a CNN architecture, is used to extract deep features from the input image while the LSTM is used to preserve the sequential structure of the video frames. The method was tested on a data set consisting of 50 repetitions of 150 signs commonly used in daily activities performed by three signers. The proposed method achieved an overall recognition accuracy of 95.9\% for the signer-dependent case, and 43.62\% for the more difficult signer-independent case.",
keywords = "Arabic sign language recognition, CNN, LSTM",
author = "Wael Suliman and Mohamed Deriche and Hamzah Luqman and Mohamed Mohandes",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2021 ; Conference date: 06-12-2021 Through 08-12-2021",
year = "2021",
doi = "10.1109/ISAECT53699.2021.9668405",
language = "English",
series = "2021 4th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 4th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2021",
address = "United States",
}