  Spelling normalization for low resource languages is a challenging task
because the patterns are hard to predict and large corpora are usually required
to collect enough examples. This work shows a comparison of a neural model and
character language models with varying amounts on target language data. Our
usage scenario is interactive correction with nearly zero amounts of training
examples, improving models as more data is collected, for example within a chat
app. Such models are designed to be incrementally improved as feedback is given
from users. In this work, we design a knowledge-base and prediction model
embedded system for spelling correction in low-resource languages. Experimental
results on multiple languages show that the model could become effective with a
small amount of data. We perform experiments on both natural and synthetic
data, as well as on data from two endangered languages (Ainu and Griko). Last,
we built a prototype system that was used for a small case study on Hinglish,
which further demonstrated the suitability of our approach in real world
scenarios.
