# indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages

arXiv: https://arxiv.org/abs/2203.16825 · HTML: https://mlsanity.com/paper/2203.16825/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [NeMo Inverse Text Normalization: From Development To Production](https://mlsanity.com/paper/2104.05055/) (2021)
- [Adversarial Transfer Learning for Punctuation Restoration](https://mlsanity.com/paper/2004.00248/) (2020)

## Built on by (most cited first, 4 of 4)

- [IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages](https://mlsanity.com/paper/2403.06350/) (2024)
- [Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages](https://mlsanity.com/paper/2411.04699/) (2024)
- [Mark My Words: A Robust Multilingual Model for Punctuation in Text and Speech Transcripts](https://mlsanity.com/paper/2506.03793/) (2025)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) generates text which is most of the times devoid of any punctuation. Absence of punctuation is text can affect readability. Also, down stream NLP tasks such as sentiment analysis, machine translation, greatly benefit by having punctuation and sentence boundary information. We present an approach for automatic punctuation of text using a pretrained IndicBERT model. Inverse text normalization is done by hand writing weighted finite state transducer (WFST) grammars. We have developed this tool for 11 Indic languages namely Hindi, Tamil, Telugu, Kannada, Gujarati, Marathi, Odia, Bengali, Assamese, Malayalam and Punjabi. All code and data is publicly. available