# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 6 Conclusions and Future Works  
This research paper investigated various methods for recognizing Arabic Sign Language (ArSL) using transfer learning. The study utilized pretrained models, originally designed for image classification tasks like VGG, ResNet, MobileNet, Xception, Inception, InceptionResNet, and DenseNet. A pretrained Google Research model named BiT, Big Transfer, was also evaluated on our dataset. Additionally, the paper explored state-of-the-art vision transformers, specifically ViT and Swin, for the task at hand. Different deep learning CNN-based architectures were also employed and compared against the pretrained models and vision transformers. Experimental results revealed that the transfer learning approach, using both pretrained models and vision transformers, achieved a higher accuracy compared to traditional CNN-based deep learning models. Although the pretrained models performed better in terms of accuracy, the vision transformers exhibited more consistent learning. The findings suggest that using recently designed pretrained models such as BiT and InceptionResNet, as well as vision transformers like ViT and Swin, can enhance accuracy, parameter efficiency, feature reuse, scalability, and overall performance in ArSL classification tasks. These advantages make these models a promising choice for future ArSL classification tasks involving words and sentences in image and video datasets. In particular, transfer learning can be a valuable tool for image classification tasks in other sign languages with limited resources and data, outperforming traditional CNN deep learning models in many scenarios. Some limitations of this work are as follows. The dataset was not representative enough to capture the complexity and variability of ArSL words and sentences as a whole. A larger and more diverse dataset could potentially provide a more comprehensive evaluation. Furthermore, practical implementation challenges such as computational resources, model deployment, real-time performance, and  
Figure 10: Performance of baseline ResNet152 [40] and its variant ResNet50. user usability could potentially pose challenges that need to be addressed for real-world applications. We propose several areas of future research that can enhance the progress of ArSL recognition and sign language understanding. These suggestions can also offer valuable guidance for the creation of robust and efficient models for other sign languages. Some of the recommended future works are as follows:  
* Address the imbalance in datasets: Imbalanced datasets can affect the performance of deep learning models. Future work can focus on handling the class imbalance present in ArSL datasets to ensure a fair representation and improve the accuracy of minority classes. * Extend the research to video-based ArSL recognition: The paper focused on ArSL recognition in images, but future work can expand the research to video-based ArSL recognition. This would involve considering temporal information and exploring techniques such as 3D convolutional networks or temporal transformers to capture motion and dynamic features in sign language videos. * Investigate hybrid approaches: Explore hybrid approaches that combine both pre-trained models and vision transformers. Investigate ways to leverage the strengths of both architectures to achieve an even higher accuracy and stability in ArSL recognition tasks. This could involve combining features from pretrained models with the attention mechanisms of vision transformers. * Further investigate the fine-tuning strategies and optimization techniques for transfer learning with pretrained models and vision transformers. Explore different hyperparameter settings, learning rate schedules, and regularization methods to improve the performance and stability of the models. * Explore techniques for data augmentation and generation specifically tailored for ArSL recognition. This can help overcome the challenge of limited data and enhance the performance and generalization of the models.