Estimating Post-OCR Denoising Complexity on Numerical Texts
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Abstract (arXiv)
Post-OCR processing has significantly improved over the past few years. However, these have been primarily beneficial for texts consisting of natural, alphabetical words, as opposed to documents of numerical nature such as invoices, payslips, medical certificates, etc. To evaluate the OCR post-processing difficulty of these datasets, we propose a method to estimate the denoising complexity of a text and evaluate it on several datasets of varying nature, and show that texts of numerical nature have a significant disadvantage. We evaluate the estimated complexity ranking with respect to the error rates of modern-day denoising approaches to show the validity of our estimator.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 3 Jul 2023.
Builds on
9 of 29 references.
References · 9
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- 2021Representing Numbers in NLP: a Survey and a Visioncited by 83
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Citing papers per year: 2024: 1.
1 paper.
- 2024Confidence-Aware Document OCR Error Detectioncited by 2
- 2024Confidence-Aware Document OCR Error Detection6 Sep 2024