The visual digital turn: Using neural networks to study historical images.
Digital humanities research has focused primarily on the analysis of texts. This emphasis stems from the availability of technology to study digitized text. Optical character recognition allows researchers to use keywords to search and analyze digitized texts. However, archives of digitized sources...
| Published in: | Digital Scholarship in the Humanities Vol. 35; no. 1; pp. 194 - 208 |
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| Main Authors: | , |
| Format: | Article |
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Oxford University Press / USA
Apr2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=142636785&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 142636785 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2020 vid: 35 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 142636785 10.1093/llc/fqy085 ppf: 194 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P size: 870KB tig: atl: The visual digital turn: Using neural networks to study historical images. aug: au: Wevers, Melvin Smits, Thomas affil: DHLab, KNAW Humanities Cluster, Amsterdam, The Netherlands Department of Cultural Studies, Radboud University, Nijmegen, The Netherlands su: Artificial neural networks Optical character recognition History Historical source material Keyword searching Fusiform gyrus sug: subj: Artificial neural networks Optical character recognition History Historical source material Keyword searching Fusiform gyrus ab: Digital humanities research has focused primarily on the analysis of texts. This emphasis stems from the availability of technology to study digitized text. Optical character recognition allows researchers to use keywords to search and analyze digitized texts. However, archives of digitized sources also contain large numbers of images. This article shows how convolutional neural networks (CNNs) can be used to categorize and analyze digitized historical visual sources. We present three different approaches to using CNNs for gaining a deeper understanding of visual trends in an archive of digitized Dutch newspapers. These include detecting medium-specific features (separating photographs from illustrations), querying images based on abstract visual aspects (clustering visually similar advertisements), and training a neural network based on visual categories developed by domain experts. We argue that CNNs allow researchers to explore the visual side of the digital turn. They allow archivists and researchers to classify and spot trends in large collections of digitized visual sources in radically new ways. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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