Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models
<|reference_start|>Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models: 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.<|reference_end|>
@article{balat2024advanced,
    title={Advanced Arabic Alphabet Sign Language Recognition Using Transfer
  Learning and Transformer Models},
    author={Mazen Balat, Rewaa Awaad, Hend Adel, Ahmed B. Zaky, Salah A. Aly},
    journal={IEEE ICCA 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00681},
    primaryClass={cs.CV cs.AI cs.LG}
}