# RoundTripOCR: A Data Generation Technique for Enhancing Post-OCR Error Correction in Low-Resource Devanagari Languages

arXiv: https://arxiv.org/abs/2412.15248 · HTML: https://mlsanity.com/paper/2412.15248/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://mlsanity.com/paper/2109.02903/) (2021)
- [OCR Post Correction for Endangered Language Texts](https://mlsanity.com/paper/2011.05402/) (2020)
- [Can Automatic Post-Editing Improve NMT?](https://mlsanity.com/paper/2009.14395/) (2020)
- [Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing](https://mlsanity.com/paper/1605.04800/) (2016)
- [Neural OCR Post-Hoc Correction of Historical Corpora](https://mlsanity.com/paper/2102.00583/) (2021)
- [Lexically Aware Semi-Supervised Learning for OCR Post-Correction](https://mlsanity.com/paper/2111.02622/) (2021)
- [Advancing Post-OCR Correction: A Comparative Study of Synthetic Data](https://mlsanity.com/paper/2408.02253/) (2024)

## Built on by (most cited first, 3 of 3)

- [PsOCR: Benchmarking Large Multimodal Models for Optical Character Recognition in Low-resource Pashto Language](https://mlsanity.com/paper/2505.10055/) (2025)
- [Exploring Cross-Lingual Knowledge Transfer via Transliteration-Based MLM Fine-Tuning for Critically Low-resource Chakma Language](https://mlsanity.com/paper/2510.09032/) (2025)
- [Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction](https://mlsanity.com/paper/2609.21595/) (2026)

## Abstract (arXiv)

Optical Character Recognition (OCR) technology has revolutionized the digitization of printed text, enabling efficient data extraction and analysis across various domains. Just like Machine Translation systems, OCR systems are prone to errors. In this work, we address the challenge of data generation and post-OCR error correction, specifically for low-resource languages. We propose an approach for synthetic data generation for Devanagari languages, RoundTripOCR, that tackles the scarcity of the post-OCR Error Correction datasets for low-resource languages. We release post-OCR text correction datasets for Hindi, Marathi, Bodo, Nepali, Konkani and Sanskrit. We also present a novel approach for OCR error correction by leveraging techniques from machine translation. Our method involves translating erroneous OCR output into a corrected form by treating the OCR errors as mistranslations in a parallel text corpus, employing pre-trained transformer models to learn the mapping from erroneous to correct text pairs, effectively correcting OCR errors.