  Soft spelling errors are a class of spelling mistakes that is widespread
among native Arabic speakers and foreign learners alike. Some of these errors
are typographical in nature. They occur due to orthographic variations of some
Arabic letters and the complex rules that dictate their correct usage. Many
people forgo these rules, and given the identical phonetic sounds, they often
confuse such letters. In this paper, we propose a bidirectional long short-term
memory network that corrects this class of errors. We develop, train, evaluate,
and compare a set of BiLSTM networks. We approach the spelling correction
problem at the character level. We handle Arabic texts from both classical and
modern standard Arabic. We treat the problem as a one-to-one sequence
transcription problem. Since the soft Arabic errors class encompasses omission
and addition mistakes, to preserve the one-to-one sequence transcription, we
propose a simple low-resource yet effective technique that maintains the
one-to-one sequencing and avoids using a costly encoder-decoder architecture.
We train the BiLSTM models to correct the spelling mistakes using transformed
input and stochastic error injection approaches. We recommend a configuration
that has two BiLSTM layers, uses the dropout regularization, and is trained
using the latter training approach with error injection rate of 40%. The best
model corrects 96.4% of the injected errors and achieves a low character error
rate of 1.28% on a real test set of soft spelling mistakes.
