# OCR Error Correction Using Character Correction and Feature-Based Word Classification

arXiv: https://arxiv.org/abs/1604.06225 · HTML: https://mlsanity.com/paper/1604.06225/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2016.

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

- [DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/2304.12484/) (2023)
- [Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers](https://mlsanity.com/paper/2103.07538/) (2021)
- [Toward a Period-Specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts](https://mlsanity.com/paper/2307.16213/) (2023)
- [Cleaning Dirty Books: Post-OCR Processing for Previously Scanned Texts](https://mlsanity.com/paper/2110.11934/) (2021)
- [A Combination of BERT and Transformer for Vietnamese Spelling Correction](https://mlsanity.com/paper/2405.02573/) (2024)
- [Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System](https://mlsanity.com/paper/2402.17204/) (2024)
- [Efficient Domain Adaptation for Text Line Recognition via Decoupled Language Models](https://mlsanity.com/paper/2603.28028/) (2026)

## Abstract (arXiv)

This paper explores the use of a learned classifier for post-OCR text correction. Experiments with the Arabic language show that this approach, which integrates a weighted confusion matrix and a shallow language model, improves the vast majority of segmentation and recognition errors, the most frequent types of error on our dataset.