  Spelling correction is the task of identifying spelling mistakes, typos, and
grammatical mistakes in a given text and correcting them according to their
context and grammatical structure. This work introduces "AraSpell," a framework
for Arabic spelling correction using different seq2seq model architectures such
as Recurrent Neural Network (RNN) and Transformer with artificial data
generation for error injection, trained on more than 6.9 Million Arabic
sentences. Thorough experimental studies provide empirical evidence of the
effectiveness of the proposed approach, which achieved 4.8% and 1.11% word
error rate (WER) and character error rate (CER), respectively, in comparison
with labeled data of 29.72% WER and 5.03% CER. Our approach achieved 2.9% CER
and 10.65% WER in comparison with labeled data of 10.02% CER and 50.94% WER.
Both of these results are obtained on a test set of 100K sentences.
