Making History Readable

Abstract

The Virginia Tech University Libraries (VTUL) Digital Library Platform (DLP) hosts digital collections that offer our users access to a wide variety of documents of historical and cultural importance. These collections are not only of academic importance but also provide our users with a glance at local historical events. Our DLP contains collections comprising digital objects featuring complex layouts, faded imagery, and hard-to-read handwritten text, which makes providing online access to these materials challenging. To address these issues, we integrate AI into our DLP workflow and convert the text in the digital objects into a machine-readable format. To enhance the user experience with our historical collections, we use custom AI agents for handwriting recognition, text extraction, and large language models (LLMs) for summarization. This poster highlights three collections focusing on handwritten letters, newspapers, and digitized topographic maps. We discuss the challenges with each collection and detail our approaches to address them. Our proposed methods aim to enhance the user experience by making the contents in these collections easier to search and navigate.

Citation

BibTeX

@inproceedings{banerjee2024making,
  author = {Banerjee, Bipasha and Goyne, Jennifer and Ingram, William A.},
  title = {Making History Readable},
  booktitle = {2024 {IEEE} International Conference on Big Data},
  series = {BigData '24},
  year = {2024},
  pages = {8620--8622},
  keywords = {Handwriting recognition; Navigation; Large language models; Layout; Focusing; User experience; Libraries; History; Data mining; Cultural differences; digital libraries; text extraction; artificial intelligence; machine learning},
  doi = {10.1109/BigData62323.2024.10826028},
  publisher = {{IEEE}},
  location = {Washington, DC, USA},
  month = dec,
  date = {15-18},
  issn = {2573-2978},
  podissn = {2639-1589},
  note = {Poster Presentation},
  month_numeric = {12}
}