<![CDATA[Automatic sign language recognition using deep learning, particularly Convolutional Neural Networks (CNNs), has shown significant potential. The ResNet architecture, through transfer learning, is frequently reported to achieve high accuracy for Arabic Sign Language Alphabet classification under ideal conditions. However, the robustness of these models against real-world visual distortions remains a significant, yet under-explored challenge. This research aims to develop a ResNet-50-based classification model while comprehensively analyzing its robustness. The primary contribution of this research is mapping the tolerance limits and the extent of performance degradation of the ResNet architecture when facing image degradation. Evaluation was conducted on both ideal test data and test data digitally modified to simulate underwater visual effects. This underwater simulation was selected as an extreme stress test scenario because it technically represents an accumulation of simultaneous real-world optical distortions, such as contrast reduction, turbidity (haziness), and light refraction. Quantitative evaluation results show that the model performs excellently with an accuracy of 96.95% under ideal conditions. However, exposure to underwater distortion resulted in an accuracy drop of 4.24%, reducing it to 92.71%. Despite this noticeable performance reduction, the model maintained an F1-Score of 92.79%. These findings provide empirical evidence regarding the capability limits of the ResNet architecture when facing visual degradation, while also emphasizing the importance of robustness testing before deep learning models can be reliably deployed in non-ideal environments full of visual uncertainties.]]>
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