Almjally et al. analyzed the efficiency of the Attention-Driven Hybrid Deep Learning Model with Feature Fusion (AHDLMFF-ASLR) for sign language recognition. Specifically, the model was implemented to improve spontaneous and accurate recognition of the sign language gestures to improve mute and deaf communication. The researchers used CLAHE and Canny Edge Detection software to highlight the sign language gestures by highlighting edges and enhancing image details. After image pre-processing, Almjally et al. utilized three deep learning models to extract the specific features of each sign language image. Swin Transformer (ST) was utilized to break each sign language image into smaller windows to capture global spatial features of each distinct part of the image; for example, identifying how hand shape correlates with torso size. ConvNeXt-Large was utilized to take larger windows of the sign language imaging, allowing the network to see a wider receptive field to better understand global patterns. Finally, ResNet50 was used to shortcut layers on the imaging, using skip connections to allow gradients to flow easier. The study was unique in the way that it fused multiple deep learning models to produce a more accurate recognition of sign language gestures; the results revealed that the model had achieved 98.10% accuracy, which outperformed numerous existing methods in the past.