Image Analysis for Archival Discovery

Image Analysis for Archival Discovery (Aida) responds to two issues within the digital humanities and digital library communities: First, that we leverage little of the information potential of the millions of images that we are creating as we digitize the cultural record. Second, that locating relevant materials in digital collections is often already a difficult endeavor and will become increasingly so as more content is digitized. We are asking what more we can do with the millions of images that represent the digitized cultural record, and we are interested in the types of discovery that serious attention to digital images might yield. More . . .

To get at this larger question we've started with a slightly smaller one: Can we identify poetic content in the historic newspapers included in Chronicling America? We anticipate the methods developed in this phase of the project will have significance in several areas, including in the reappraisal of conventional narratives of the history and experience of poetry in American culture; in advancing work on dealing with multi-language corpora, since we're looking at visual elements rather than linguistic features; in thinking about how we can aid discovery of materials in heterogeneous collections; and in developing the possibilities of visual analytics for discovery in digital library collections. Less . . .

Project Team

  • Maanas Varma Datla, 2014–2015
  • Spencer Kulwicki, 2014–2015
  • Grace Thomas, 2013–2015

Contact

  • llorang2@unl.edu
  • lksoh@cse.unl.edu

News & Updates

Documents

  • Interim report, submitted to National Endowment for the Humanities, January 2016
  • Interim report, submitted to National Endowment for the Humanities, June 2015
  • Interim report, submitted to National Endowment for the Humanities, January 2015
  • Application for start-up funding, submitted to National Endowment for the Humanities, Office of Digital Humanities, September 2013

Code & Data

  • Project code will be available on GitHub
  • Project data will be made available via the UNL data repository