Automatic recognition of Arabic alphabets sign language using deep learning

Abstract

<span>Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know.</span>

Toward a Vision-Based Intelligent System: A Stacked Encoded Deep Learning Framework for Sign Language Recognition

“…However, they used a data augmentation approach to increase the model performance. It can also be seen that Duwairi et al [12] proposed VGGNET and obtained promising performance. Alani et al [45] achieved higher performance of 96.59 and 97.29% accuracy with and without the data augmentation approach, respectively.…”

“…The results demonstrated that the Histogram of Oriented Gradients obtained promising performance, using One-Versus-All SVM and HOG identifiers. The Kinect sensor was used in [12] to develop a real-time automatic Arabic SL recognition system based on the Dynamic Time Warping coordination approach. Power and data gloves are not used by the software.…”

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

“…ArSL is difcult to recognize automatically; however, a new framework suggested by Duwairi and Halloush. [63] may help. Popular deep-learning models (such as AlexNet, VGGNet, and Inception Net) are used in this framework to do image processing through transfer learning.…”

Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models

“…Te study [34] also proposed a framework based on a variety of deep learning models for the automatic recognition of Arabic Sign Language, specifcally by using AlexNet, VGGNet, and GoogLeNet/Inception models in training and evaluating the efectiveness of shallow learning techniques using nearest neighbors and SVM algorithms as baselines. Te suggested algorithm provided encouraging results in detecting Arabic Sign Language with a 97% accuracy rate.…”