Exploring CNN-based transfer learning approaches for Arabic alphabets sign language recognition using the ArSL2018 dataset

Arabic alphabets sign language (ArASL) recognition is an important topic that has gotten insufficient attention regardless of its significance in the Arab world. This research compares CNN-based transfer learning models for Arabic alphabets sign language (ArASL) recognition using the ArSL2018 dataset, which comprises 54,049 pictures representing 32 sign and letter classes. Three pre-trained models are examined (InceptionV3, VGG16, and MobileNetV2) and compared using a training and evaluation dataset split. We use transfer learning to fine-tune these models on the ArSL2018 dataset and compare their performance. Our experimental findings indicate that the MobileNetV2 model exceeds the other models in terms of accuracy, achieving an overall accuracy of 96%, which exceeds the state-of-the-art results, reported in previous works. Our study demonstrates that transfer learning is an effective approach for recognising Arabic alphabets sign language using CNN-based models and provides insights into the suitability of different pre-trained models for this task.