TY - GEN
T1 - AIDA
T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
AU - Soh, Leen Kiat
AU - Lorang, Elizabeth
AU - Liu, Yi
N1 - 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:
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060487620&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060487620&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85060487620
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 7837
EP - 7842
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI Press
Y2 - 2 February 2018 through 7 February 2018
ER -