  Writing, as an omnipresent form of human communication, permeates nearly
every aspect of contemporary life. Consequently, inaccuracies or errors in
written communication can lead to profound consequences, ranging from financial
losses to potentially life-threatening situations. Spelling mistakes, among the
most prevalent writing errors, are frequently encountered due to various
factors. This research aims to identify and rectify diverse spelling errors in
text using neural networks, specifically leveraging the Bidirectional Encoder
Representations from Transformers (BERT) masked language model. To achieve this
goal, we compiled a comprehensive dataset encompassing both non-real-word and
real-word errors after categorizing different types of spelling mistakes.
Subsequently, multiple pre-trained BERT models were employed. To ensure optimal
performance in correcting misspelling errors, we propose a combined approach
utilizing the BERT masked language model and Levenshtein distance. The results
from our evaluation data demonstrate that the system presented herein exhibits
remarkable capabilities in identifying and rectifying spelling mistakes, often
surpassing existing systems tailored for the Persian language.
