Understanding the application of handwritten text recognition technology in heritage contexts: a systematic review of Transkribus in published research
Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, re...
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| Vydáno v: | Archival science Ročník 22; číslo 3; s. 367 - 392 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Dordrecht
Springer Netherlands
01.09.2022
Springer Nature B.V |
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| ISSN: | 1389-0166, 1573-7500, 1573-7500, 1573-7519 |
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| Abstract | Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were
humanities applications
(67%),
technological (25%), users
(5%) and
tutorials (3%)
. This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment. |
|---|---|
| AbstractList | Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were
humanities applications
(67%),
technological (25%), users
(5%) and
tutorials (3%)
. This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment. Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were humanities applications (67%), technological (25%), users (5%) and tutorials (3%). This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment.Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were humanities applications (67%), technological (25%), users (5%) and tutorials (3%). This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment. Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were humanities applications (67%), technological (25%), users (5%) and tutorials (3%). This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment. |
| Author | Gooding, Paul Nockels, Joe Terras, Melissa Ames, Sarah |
| Author_xml | – sequence: 1 givenname: Joe orcidid: 0000-0002-4577-6596 surname: Nockels fullname: Nockels, Joe email: J.H.Nockels@sms.ed.ac.uk organization: School of Literatures, Languages and Cultures, University of Edinburgh – sequence: 2 givenname: Paul orcidid: 0000-0003-1044-509X surname: Gooding fullname: Gooding, Paul organization: Information Studies, College of Arts, University of Glasgow – sequence: 3 givenname: Sarah orcidid: 0000-0002-0118-189X surname: Ames fullname: Ames, Sarah organization: National Library of Scotland – sequence: 4 givenname: Melissa orcidid: 0000-0001-6496-3197 surname: Terras fullname: Terras, Melissa organization: College of Arts, Humanities and Social Sciences, University of Edinburgh |
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| Cites_doi | 10.17723/aarc.76.2.lh76217m2m376n28 10.1108/00220411211256021 10.7551/mitpress/11404.001.0001 10.1109/ICFHR-2018.2018.00098 10.1525/tph.2004.26.1.1 10.1353/emw.2018.0056 10.1108/JD-07-2018-0114 10.1016/j.patcog.2019.05.025 10.1145/3178876.3186136 10.1108/JD-03-2012-0027 10.1109/DAS.2018.15 10.5250/femigermstud.36.1.0054 10.1371/journal.pone.0238372 10.4135/9781452230153 10.1108/00220410910970267 10.11647/OBP.0095.09 10.29085/9781783303588 10.1353/bh.2017.0006 10.1109/DAS.2018.38 10.1093/acprof:oso/9780190215491.001.0001 10.1007/978-981-13-5802-9_59 10.18420/infdh2018-13 10.4135/9781412983488 |
| ContentType | Journal Article |
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| Keywords | Handwritten text recognition Digitisation Artificial intelligence Digital library Systematic literature review Transkribus |
| Language | English |
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