# From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction

arXiv: https://arxiv.org/abs/1910.05535 · HTML: https://mlsanity.com/paper/1910.05535/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Lexically Aware Semi-Supervised Learning for OCR Post-Correction](https://mlsanity.com/paper/2111.02622/) (2021)
- [Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling](https://mlsanity.com/paper/2105.11872/) (2021)
- [Neural Language Models for Nineteenth-Century English](https://mlsanity.com/paper/2105.11321/) (2021)
- [An Assessment of the Impact of OCR Noise on Language Models](https://mlsanity.com/paper/2202.00470/) (2022)
- [Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription](https://mlsanity.com/paper/2112.08692/) (2021)
- [Toward a Period-Specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts](https://mlsanity.com/paper/2307.16213/) (2023)
- [Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation](https://mlsanity.com/paper/2407.18562/) (2024)

## Abstract (arXiv)

A great deal of historical corpora suffer from errors introduced by the OCR (optical character recognition) methods used in the digitization process. Correcting these errors manually is a time-consuming process and a great part of the automatic approaches have been relying on rules or supervised machine learning. We present a fully automatic unsupervised way of extracting parallel data for training a character-based sequence-to-sequence NMT (neural machine translation) model to conduct OCR error correction.