Finding Old Images through a New Lens: Use of Computer Vision for Searching Historic Newspaper Collections

Abstract

The capacity to find images with granularity—i.e., finding images through image-based searching—is requisite to increase the usefulness of image-based research for the digital humanities. As evident through the National Endowment for the Humanities’s data challenge, which invited scholars and students “to produce creative web-based projects demonstrating the potential for using the [textual] data found in Chronicling America,” digital newspaper collections are a rich source for digital humanities research. Chronicling America’s search interface and application programming interface (API), however, are restricted to text-based searches, therein limiting the findability of image-based content. In their paper on “Library Collections as Humanities Data: The Facet Effect,” Thomas Padilla and Devin Higgins elucidate the value of images as a resource which might meet digital humanists’ inquiry of images as a substantive source for research. The use of computer vision for querying image-based digital collections enables users to find image content which, due to the lack of image tagging and description, might not otherwise be found through keyword searching. For example, in a digitized newspaper collection a user might query the image of an advertisement for spectacles which would not return through a keyword search.

The authors propose a case study that applies computer vision image searching to the Farm, Field, and Fireside Collection, a collection of 22 historic agricultural newspapers...