Optical character recognition (OCR) is typically used to extract the textual contents of
scanned texts. The output of OCR can be noisy, especially when the quality of the scanned …

This paper explores the application of synthetic data in the post-OCR domain on multiple
fronts by conducting experiments to assess the impact of data volume, augmentation, and …

The efficacy of large-scale language models (LLMs) as few-shot learners has dominated the
field of natural language processing, achieving state-of-the-art performance in most tasks …

In this paper, we address the detection of named entities in multilingual historical collections.
We argue that, besides the multiple challenges that depend on the quality of digitization (eg …

Archive collections are nowadays mostly available through search engines interfaces, which
allow a user to retrieve documents by issuing queries. The study of these collections may be …

Abstract Optical Character Recognition (OCR) continues to face accuracy challenges that
impact subsequent applications. To address these errors, we explore the utility of OCR …

Scientific articles published prior to the “age of digitization” in the late 1990s contain figures
which are “trapped” within their scanned pages. While progress to extract figures and their …

Many museums and libraries conducted efforts to digitize their assets, and many historic
documents are now available as digital images. However, these documents are not directly …

This paper addresses a major challenge to historical research on the 19th century. Large
quantities of sources have become digitally available for the first time, while extraction …

Scientific articles published prior to the" age of digitization"(~ 1997) require Optical
Character Recognition (OCR) to transform scanned documents into machine-readable text …