1 citations · 1 across the 4 of their papers we have counts for
4 papers
The Character Error Vector: Decomposable errors for page-level OCR evaluation
Jonathan Bourne, Mwiza Simbeye, Joseph Nockels
The Character Error Rate (CER) is a key metric for evaluating the quality of Optical Character Recognition (OCR). However, this metric assumes that text has been perfectly parsed,…
The COTe score: A decomposable framework for evaluating Document Layout Analysis models
Jonathan Bourne, Mwiza Simbeye, Ishtar Govia
Document Layout analysis (DLA), is the process by which a page is parsed into meaningful elements, often using machine learning models. Typically, the quality of a model is judged…
Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language models
Jonathan Bourne
Oscar Wilde said, "The difference between literature and journalism is that journalism is unreadable, and literature is not read." Unfortunately, The digitally archived journalism…
Scrambled text: training Language Models to correct OCR errors using synthetic data
Jonathan Bourne
OCR errors are common in digitised historical archives significantly affecting their usability and value. Generative Language Models (LMs) have shown potential for correcting these…