  While there have been several contributions exploring state of the art
techniques for text normalization, the problem of inverse text normalization
(ITN) remains relatively unexplored. The best known approaches leverage finite
state transducer (FST) based models which rely on manually curated rules and
are hence not scalable. We propose an efficient and robust neural solution for
ITN leveraging transformer based seq2seq models and FST-based text
normalization techniques for data preparation. We show that this can be easily
extended to other languages without the need for a linguistic expert to
manually curate them. We then present a hybrid framework for integrating Neural
ITN with an FST to overcome common recoverable errors in production
environments. Our empirical evaluations show that the proposed solution
minimizes incorrect perturbations (insertions, deletions and substitutions) to
ASR output and maintains high quality even on out of domain data. A transformer
based model infused with pretraining consistently achieves a lower WER across
several datasets and is able to outperform baselines on English, Spanish,
German and Italian datasets.
