1 citations · 2 across the 5 of their papers we have counts for
5 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,…
MOTe error: A decomposable framework for evaluating Document Layout Analysis models
Jonathan Bourne, Mwiza Simbeye, Ishtar Govia
Document Layout Analysis (DLA) is a machine vision task that parses a page into meaningful elements, and is a critical stage in Document Understanding (DU). DLA models are usually…
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…
CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language Models
Jonathan Bourne
The digitisation of historical print media archives is crucial for increasing accessibility to contemporary records. However, the process of Optical Character Recognition (OCR) use…