Transfer Learning and Recurrent Neural Networks for Automatic Arabic Sign Language Recognition

Elsayed, Mahmoud; Wassif, Khaled; Bayomi, Hanaa

doi:10.1007/978-3-031-03918-8_5

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Te classifcation results generated by the suggested lightweight models were then evaluated using the proper performance indicators. Mahmoud et al [23] developed an architecture that integrates transfer learning (TL) models and recurrent neural network (RNN) models for ArSL recognition. Te results achieved in this work have a peak recognition accuracy of 93.4%.…”

Section: Related Workmentioning

confidence: 99%

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

Bani Baker,

Alqudah,

Alsmadi

et al. 2023

Applied Computational Intelligence and Soft Computing

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Sign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are numerous signs, each characterized by differences in hand shapes, hand positions, motions, facial expressions, and body parts used to convey specific meanings. The complexity of visual sign language recognition poses a significant challenge in the computer vision research area. This study presents an Arabic Sign Language recognition (ArSL) system that utilizes convolutional neural networks (CNNs) and several transfer learning models to automatically and accurately identify Arabic Sign Language characters. The dataset used for this study comprises 54,049 images of ArSL letters. The results of this research indicate that InceptionV3 outperformed other pretrained models, achieving a remarkable 100% accuracy score and a 0.00 loss score without overfitting. These impressive performance measures highlight the distinct capabilities of InceptionV3 in recognizing Arabic characters and underscore its robustness against overfitting. This enhances its potential for future research in the field of Arabic Sign Language recognition.

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Section: Related Workmentioning

confidence: 99%

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

Bani Baker,

Alqudah,

Alsmadi

et al. 2023

Applied Computational Intelligence and Soft Computing

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“…[19] and [20] provide a Transfer Learning approach for the Arabic Sign Language fingerspelling alphabet. Authors of [19] used RNN architecture to boost accuracy. In [20], Keras pretrained models with EfficientNet architecture were used.…”

Section: Related Workmentioning

confidence: 99%

Transfer learning for Azerbaijani Sign Language Recognition

Abdullayeva¹,

Alishzade²

2022

Informatics and Control Problems

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The goal of sign language technologies is to develop a bridging solution for the communication gap between the hearing-impaired community and the rest of society. Real-time Sign Language Recognition (SLR) is a state-of-the-art subject that promises to facilitate communication between the hearing-impaired community and others. Our research uses transfer learning to provide vision-based sign language recognition. We investigated recent works that use CNN-based methods and provided a literature review on deep learning systems for the sign language recognition (SLR) problem. This paper discusses the architecture of deep learning methods for SLR systems and explains a transfer learning application for fingerspelling sign classification. For the experiments, we used the Azerbaijani Sign Language Fingerspelling dataset and got 88.0% accuracy.

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Intelligent Signs Language Understanding with Autonomous Landmarks for E-learning Context

Hussain

Shaoor

2022

2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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Transfer Learning and Recurrent Neural Networks for Automatic Arabic Sign Language Recognition

Cited by 4 publications

References 26 publications

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

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

Transfer learning for Azerbaijani Sign Language Recognition

Intelligent Signs Language Understanding with Autonomous Landmarks for E-learning Context

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