# OCR Post Correction for Endangered Language Texts

arXiv: https://arxiv.org/abs/2011.05402 · HTML: https://mlsanity.com/paper/2011.05402/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 30 of 31)

- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [Robust Open-Vocabulary Translation from Visual Text Representations](https://mlsanity.com/paper/2104.08211/) (2021)
- [Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation](https://mlsanity.com/paper/2203.09435/) (2022)
- [XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages](https://mlsanity.com/paper/2305.11938/) (2023)
- [Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks](https://mlsanity.com/paper/2105.00573/) (2021)
- [A Material Lens on Coloniality in NLP](https://mlsanity.com/paper/2311.08391/) (2023)
- [How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language](https://mlsanity.com/paper/2204.11909/) (2022)
- [GlotScript: A Resource and Tool for Low Resource Writing System Identification](https://mlsanity.com/paper/2309.13320/) (2023)
- [Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens](https://mlsanity.com/paper/2202.07036/) (2022)
- [Lexically Aware Semi-Supervised Learning for OCR Post-Correction](https://mlsanity.com/paper/2111.02622/) (2021)
- [Efficient OCR for Building a Diverse Digital History](https://mlsanity.com/paper/2304.02737/) (2023)
- [Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence Models](https://mlsanity.com/paper/2109.06264/) (2021)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Extracting Training Data from Document-Based VQA Models](https://mlsanity.com/paper/2407.08707/) (2024)
- [OCR Improves Machine Translation for Low-Resource Languages](https://mlsanity.com/paper/2202.13274/) (2022)
- [PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy](https://mlsanity.com/paper/2505.20429/) (2025)
- [User-Centric Evaluation of OCR Systems for Kwak'wala](https://mlsanity.com/paper/2302.13410/) (2023)
- [Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription](https://mlsanity.com/paper/2112.08692/) (2021)
- [Post-OCR Text Correction for Bulgarian Historical Documents](https://mlsanity.com/paper/2409.00527/) (2024)
- [RoundTripOCR: A Data Generation Technique for Enhancing Post-OCR Error Correction in Low-Resource Devanagari Languages](https://mlsanity.com/paper/2412.15248/) (2024)
- [Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages](https://mlsanity.com/paper/2203.09632/) (2022)
- [synthocr-gen: A synthetic ocr dataset generator for low-resource languages- breaking the data barrier](https://mlsanity.com/paper/2601.16113/) (2026)
- [A Survey of OCR Evaluation Methods and Metrics and the Invisibility of Historical Documents](https://mlsanity.com/paper/2603.25761/) (2026)
- [Noisy Parallel Data Alignment](https://mlsanity.com/paper/2301.09685/) (2023)
- [CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models](https://mlsanity.com/paper/2404.02408/) (2024)
- [Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi](https://mlsanity.com/paper/2211.16172/) (2022)
- [DUBLIN -- Document Understanding By Language-Image Network](https://mlsanity.com/paper/2305.14218/) (2023)
- [Developing a Mixed-Methods Pipeline for Community-Oriented Digitization of Kwak'wala Legacy Texts](https://mlsanity.com/paper/2506.01775/) (2025)
- [Low-Resource Heuristics for Bahnaric Optical Character Recognition Improvement](https://mlsanity.com/paper/2601.02965/) (2026)
- [Structured Layout Priors for Robust Out-of-Distribution Visual Document Understanding](https://mlsanity.com/paper/2605.19866/) (2026)

## Abstract (arXiv)

There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34% on average across the three languages.