  The task of Spell Correction(SC) in low-resource languages presents a
significant challenge due to the availability of only a limited corpus of data
and no annotated spelling correction datasets. To tackle these challenges a
small-scale word-based transformer LM is trained to provide the SC model with
contextual understanding. Further, the probabilistic error rules are extracted
from the corpus in an unsupervised way to model the tendency of error
happening(error model). Then the combination of LM and error model is used to
develop the SC model through the well-known noisy channel framework. The
effectiveness of this approach is demonstrated through experiments on the
Nepali language where there is access to just an unprocessed corpus of textual
data.
