# Improving OCR using internal document redundancy

arXiv: https://arxiv.org/abs/2508.14557 · HTML: https://mlsanity.com/paper/2508.14557/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 20 Aug 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [Convolutional Character Networks](https://mlsanity.com/paper/1910.07954/) (2019)
- [End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940](https://mlsanity.com/paper/2404.19329/) (2024)
- [The Learnable Typewriter: A Generative Approach to Text Analysis](https://mlsanity.com/paper/2302.01660/) (2023)
- [Normalized vs Diplomatic Annotation: A Case Study of Automatic Information Extraction from Handwritten Uruguayan Birth Certificates](https://mlsanity.com/paper/2507.08636/) (2025)

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

- [MADP: A Multi-Agent Pipeline for Sustainable Document Processing with Human-in-the-Loop](https://mlsanity.com/paper/2605.17159/) (2026)
- [When Low CER is Not Enough: An Analysis of Hallucinations in Vision-Language OCR Systems on Historical Uruguayan Documents](https://mlsanity.com/paper/2607.24077/) (2026)
- [Theatre Chapbooks At Scale: A Statistical Comparative Analysis of Typography](https://mlsanity.com/paper/2607.27266/) (2026)

## Abstract (arXiv)

Current OCR systems are based on deep learning models trained on large amounts of data. Although they have shown some ability to generalize to unseen data, especially in detection tasks, they can struggle with recognizing low-quality data. This is particularly evident for printed documents, where intra-domain data variability is typically low, but inter-domain data variability is high. In that context, current OCR methods do not fully exploit each document's redundancy. We propose an unsupervised method by leveraging the redundancy of character shapes within a document to correct imperfect outputs of a given OCR system and suggest better clustering. To this aim, we introduce an extended Gaussian Mixture Model (GMM) by alternating an Expectation-Maximization (EM) algorithm with an intra-cluster realignment process and normality statistical testing. We demonstrate improvements in documents with various levels of degradation, including recovered Uruguayan military archives and 17th to mid-20th century European newspapers.