Estimating Post-OCR Denoising Complexity on Numerical Texts
Arthur Hemmer Jérôme Brachat Mickaël Coustaty Jean-Marc Ogier
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.