  Building tools for code-mixed data is rapidly gaining popularity in the NLP
research community as such data is exponentially rising on social media.
Working with code-mixed data contains several challenges, especially due to
grammatical inconsistencies and spelling variations in addition to all the
previous known challenges for social media scenarios. In this article, we
present a novel architecture focusing on normalizing phonetic typing
variations, which is commonly seen in code-mixed data. One of the main features
of our architecture is that in addition to normalizing, it can also be utilized
for back-transliteration and word identification in some cases. Our model
achieved an accuracy of 90.27% on the test data.
