# Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence Models

arXiv: https://arxiv.org/abs/2109.06264 · HTML: https://mlsanity.com/paper/2109.06264/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [OCR Post Correction for Endangered Language Texts](https://mlsanity.com/paper/2011.05402/) (2020)
- [Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks](https://mlsanity.com/paper/1610.07796/) (2016)

## Built on by (most cited first, 8 of 8)

- [Advancing Post-OCR Correction: A Comparative Study of Synthetic Data](https://mlsanity.com/paper/2408.02253/) (2024)
- [CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language Models](https://mlsanity.com/paper/2408.17428/) (2024)
- [Confidence-Aware Document OCR Error Detection](https://mlsanity.com/paper/2409.04117/) (2024)
- [Musical Heritage Historical Entity Linking](https://mlsanity.com/paper/2502.09168/) (2025)
- [Estimating Post-OCR Denoising Complexity on Numerical Texts](https://mlsanity.com/paper/2307.01020/) (2023)
- [Data Generation for Post-OCR correction of Cyrillic handwriting](https://mlsanity.com/paper/2311.15896/) (2023)
- [ATLAS: Article Tracking, Linking, and Analysis of Swedish Encyclopedias](https://mlsanity.com/paper/2605.02466/) (2026)
- [Structured Layout Priors for Robust Out-of-Distribution Visual Document Understanding](https://mlsanity.com/paper/2605.19866/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel method based on character sequence-to-sequence models to correct documents already processed with Optical Character Recognition (OCR) systems. The main contribution of this paper is a set of strategies to accurately process strings much longer than the ones used to train the sequence model while being sample- and resource-efficient, supported by thorough experimentation. The strategy with the best performance involves splitting the input document in character n-grams and combining their individual corrections into the final output using a voting scheme that is equivalent to an ensemble of a large number of sequence models. We further investigate how to weigh the contributions from each one of the members of this ensemble. We test our method on nine languages of the ICDAR 2019 competition on post-OCR text correction and achieve a new state-of-the-art performance in five of them. Our code for post-OCR correction is shared at https://github.com/jarobyte91/post_ocr_correction.