Unlocking the Digitized Historical Newspaper Archive: Exploring Historical Insights with Deep Learning.
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| Title: | Unlocking the Digitized Historical Newspaper Archive: Exploring Historical Insights with Deep Learning. |
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| Authors: | Wai-Yip Lum, Vincent1 vincentlum@cuhk.edu.hk, Kin-Fu Yip, Michael2 michaelyip@hsu.edu.hk |
| Source: | Information Technology & Libraries. Sep2025, Vol. 44 Issue 3, p1-16. 16p. |
| Subject Terms: | *Archives, *Newspapers, *Motivation (Psychology), *Storytelling, Digital diagnostic imaging, Natural language processing, Paradigms (Social sciences), Deep learning, HTML (Document markup language) |
| Abstract: | This paper aims to utilize historical newspapers through the application of computer vision and machine/deep learning to extract the headlines and illustrations from newspapers for storytelling. This endeavor seeks to unlock the historical knowledge embedded within newspaper contents while simultaneously utilizing cutting-edge methodological paradigms for research in the digital humanities (DH) realm. We targeted to provide another facet apart from the traditional search or browse interfaces and incorporated those DH tools with place- and time-based visualizations. Experimental results showed our proposed methodologies in OCR (optical character recognition) with scraping and deep learning object detection models can be used to extract the necessary textual and image content for more sophisticated analysis. Timeline and geodata visualization products were developed to facilitate a comprehensive exploration of our historical newspaper data. The timeline-based tool spanned the period from July 1942 to July 1945, enabling users to explore the evolving narratives through the lens of daily headlines. The interactive geographical tool can enable users to identify geographic hotspots and patterns. Combining both products can enrich users' understanding of the events and narratives unfolding across time and space. [ABSTRACT FROM AUTHOR] |
| Copyright of Information Technology & Libraries is the property of American Library Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 188380621 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unlocking the Digitized Historical Newspaper Archive: Exploring Historical Insights with Deep Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wai-Yip+Lum%2C+Vincent%22">Wai-Yip Lum, Vincent</searchLink><relatesTo>1</relatesTo><i> vincentlum@cuhk.edu.hk</i><br /><searchLink fieldCode="AR" term="%22Kin-Fu+Yip%2C+Michael%22">Kin-Fu Yip, Michael</searchLink><relatesTo>2</relatesTo><i> michaelyip@hsu.edu.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Information+Technology+%26+Libraries%22">Information Technology & Libraries</searchLink>. Sep2025, Vol. 44 Issue 3, p1-16. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Archives%22">Archives</searchLink><br />*<searchLink fieldCode="DE" term="%22Newspapers%22">Newspapers</searchLink><br />*<searchLink fieldCode="DE" term="%22Motivation+%28Psychology%29%22">Motivation (Psychology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Storytelling%22">Storytelling</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+diagnostic+imaging%22">Digital diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Paradigms+%28Social+sciences%29%22">Paradigms (Social sciences)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22HTML+%28Document+markup+language%29%22">HTML (Document markup language)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper aims to utilize historical newspapers through the application of computer vision and machine/deep learning to extract the headlines and illustrations from newspapers for storytelling. This endeavor seeks to unlock the historical knowledge embedded within newspaper contents while simultaneously utilizing cutting-edge methodological paradigms for research in the digital humanities (DH) realm. We targeted to provide another facet apart from the traditional search or browse interfaces and incorporated those DH tools with place- and time-based visualizations. Experimental results showed our proposed methodologies in OCR (optical character recognition) with scraping and deep learning object detection models can be used to extract the necessary textual and image content for more sophisticated analysis. Timeline and geodata visualization products were developed to facilitate a comprehensive exploration of our historical newspaper data. The timeline-based tool spanned the period from July 1942 to July 1945, enabling users to explore the evolving narratives through the lens of daily headlines. The interactive geographical tool can enable users to identify geographic hotspots and patterns. Combining both products can enrich users' understanding of the events and narratives unfolding across time and space. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Information Technology & Libraries is the property of American Library Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5860/ital.v44i3.17292 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Archives Type: general – SubjectFull: Newspapers Type: general – SubjectFull: Motivation (Psychology) Type: general – SubjectFull: Storytelling Type: general – SubjectFull: Digital diagnostic imaging Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Paradigms (Social sciences) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: HTML (Document markup language) Type: general Titles: – TitleFull: Unlocking the Digitized Historical Newspaper Archive: Exploring Historical Insights with Deep Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wai-Yip Lum, Vincent – PersonEntity: Name: NameFull: Kin-Fu Yip, Michael IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 07309295 Numbering: – Type: volume Value: 44 – Type: issue Value: 3 Titles: – TitleFull: Information Technology & Libraries Type: main |
| ResultId | 1 |
