Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models

• URL: http://arxiv.org/abs/2410.00681v1
• Date: Tue, 1 Oct 2024 13:39:26 GMT
• Title: Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models
• Authors: Mazen Balat, Rewaa Awaad, Hend Adel, Ahmed B. Zaky, Salah A. Aly,
• Abstract summary: This paper presents an Arabic Alphabet Sign Language recognition approach, using deep learning methods in conjunction with transfer learning and transformer-based models. We study the performance of the different variants on two publicly available datasets, namely ArSL2018 and AASL. Experimental results present evidence that the suggested methodology can receive a high recognition accuracy, by up to 99.6% and 99.43% on ArSL2018 and AASL, respectively.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper presents an Arabic Alphabet Sign Language recognition approach, using deep learning methods in conjunction with transfer learning and transformer-based models. We study the performance of the different variants on two publicly available datasets, namely ArSL2018 and AASL. This task will make full use of state-of-the-art CNN architectures like ResNet50, MobileNetV2, and EfficientNetB7, and the latest transformer models such as Google ViT and Microsoft Swin Transformer. These pre-trained models have been fine-tuned on the above datasets in an attempt to capture some unique features of Arabic sign language motions. Experimental results present evidence that the suggested methodology can receive a high recognition accuracy, by up to 99.6\% and 99.43\% on ArSL2018 and AASL, respectively. That is far beyond the previously reported state-of-the-art approaches. This performance opens up even more avenues for communication that may be more accessible to Arabic-speaking deaf and hard-of-hearing, and thus encourages an inclusive society.

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