https://jjcit.org/paper/215 10.5455/jjcit.71-1699768515 Mohammed Al-Qaraghuli,Ola Arif Jaafar Arabic spelling correction,Transformers,Text-to-text transfer transformer,T5,Natural-language processing 7 3871 1245 12-Nov.-2023 3-Jan.-2024 20-Jan.-2024 Spelling correction is considered a challenging task for resource-scarce languages. The Arabic language is one of these resource-scarce languages, which suffers from the absence of a large spelling correction dataset, thus datasets injected with artificial errors are used to overcome this problem. In this paper, we trained the Text-to-Text Transfer Transformer (T5) model using artificial errors to correct Arabic soft spelling mistakes. Our T5 model can correct 97.8% of the artificial errors that were injected into the test set. Additionally, our T5 model achieves a character error rate (CER) of 0.77% on a set that contains real soft spelling mistakes. We achieved these results using a 4-layer T5 model trained with a 90% error injection rate, with a maximum sequence length of 300 characters.