# Confidence-Aware Document OCR Error Detection

arXiv: https://arxiv.org/abs/2409.04117 · HTML: https://mlsanity.com/paper/2409.04117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [PP-OCR: A Practical Ultra Lightweight OCR System](https://mlsanity.com/paper/2009.09941/) (2020)
- [Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers](https://mlsanity.com/paper/1805.08154/) (2018)
- [A Survey of Deep Learning Approaches for OCR and Document Understanding](https://mlsanity.com/paper/2011.13534/) (2020)
- [CLEval: Character-Level Evaluation for Text Detection and Recognition Tasks](https://mlsanity.com/paper/2006.06244/) (2020)
- [Post-OCR Document Correction with large Ensembles of Character Sequence-to-Sequence Models](https://mlsanity.com/paper/2109.06264/) (2021)
- [An Assessment of the Impact of OCR Noise on Language Models](https://mlsanity.com/paper/2202.00470/) (2022)
- [Detection Masking for Improved OCR on Noisy Documents](https://mlsanity.com/paper/2205.08257/) (2022)
- [Text Detection Forgot About Document OCR](https://mlsanity.com/paper/2210.07903/) (2022)
- [Unknown-box Approximation to Improve Optical Character Recognition Performance](https://mlsanity.com/paper/2105.07983/) (2021)
- [Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach](https://mlsanity.com/paper/2401.07787/) (2024)
- [Estimating Post-OCR Denoising Complexity on Numerical Texts](https://mlsanity.com/paper/2307.01020/) (2023)

## Built on by (most cited first, 2 of 2)

- [Beyond Logprobs: A Multi-Signal Confidence Engine for LLM-Based Document Field Extraction](https://mlsanity.com/paper/2606.24420/) (2026)
- [Improving MLLM Historical Record Extraction with Test-Time Image](https://mlsanity.com/paper/2509.09722/) (2025)

## Abstract (arXiv)

Optical Character Recognition (OCR) continues to face accuracy challenges that impact subsequent applications. To address these errors, we explore the utility of OCR confidence scores for enhancing post-OCR error detection. Our study involves analyzing the correlation between confidence scores and error rates across different OCR systems. We develop ConfBERT, a BERT-based model that incorporates OCR confidence scores into token embeddings and offers an optional pre-training phase for noise adjustment. Our experimental results demonstrate that integrating OCR confidence scores can enhance error detection capabilities. This work underscores the importance of OCR confidence scores in improving detection accuracy and reveals substantial disparities in performance between commercial and open-source OCR technologies.