@inproceedings{b36a039279f14481a09b6551b4f616ce,
title = "Aida: Intelligent image analysis to automatically detect poems in digital archives of historic newspapers",
abstract = "We describe an intelligent image analysis approach to automatically detect poems in digitally archived historic newspapers. Our application, Image Analysis for Archival Discovery, or Aida, integrates computer vision to capture visual cues based on visual structures of poetic works- instead of the meaning or content-and machine learning to train an artificial neural network to determine whether an image has poetic text. We have tested our application on almost 17,000 image snippets and obtained promising accuracies, precision, and recall. The application is currently being deployed at two institutions for digital library and literary research.",
author = "Soh, {Leen Kiat} and Elizabeth Lorang and Yi Liu",
note = "Funding Information: This project is supported in part by the Institute of Museum and Library Services (IMLS) and has received previous support from the National Endowment for the Humanities (NEH). Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of IMLS or NEH. Maanas Varma Datla, Spencer Kulwicki, and Grace Thomas made early contributions to this project that shaped its development. Publisher Copyright: {\textcopyright} 2018 Proceedings of the 30th Innovative Applications of Artificial Intelligence Conference, IAAI 2018. All rights reserved.; 30th Innovative Applications of Artificial Intelligence Conference, IAAI 2018 ; Conference date: 02-02-2018 Through 07-02-2018",
year = "2018",
language = "English (US)",
series = "Proceedings of the 30th Innovative Applications of Artificial Intelligence Conference, IAAI 2018",
publisher = "The AAAI Press",
pages = "7837--7842",
editor = "Youngblood, {G. Michael} and Karen Myers",
booktitle = "Proceedings of the 30th Innovative Applications of Artificial Intelligence Conference, IAAI 2018",
}