A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources

This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longe...

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Main Authors: Gunnar Thorvaldsen, Joana Maria Pujadas-Mora, Trygve Andersen, Line Eikvil, Josep Lladós, Alícia Fornés, Anna Cabré
Format: Article
Language:English
Published: International Instititute of Social History 2015-01-01
Series:Historical Life Course Studies
Subjects:
Online Access:https://hlcs.nl/article/view/9355
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spelling doaj-b53a019907974125814ea87c5721e33d2021-06-11T10:09:10ZengInternational Instititute of Social HistoryHistorical Life Course Studies2352-63432015-01-012A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical SourcesGunnar ThorvaldsenJoana Maria Pujadas-MoraTrygve AndersenLine EikvilJosep LladósAlícia FornésAnna Cabré This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources. https://hlcs.nl/article/view/9355Word spottingOptical Character RecognitionVital recordsCensusNominative sourcesComputer vision
collection DOAJ
language English
format Article
sources DOAJ
author Gunnar Thorvaldsen
Joana Maria Pujadas-Mora
Trygve Andersen
Line Eikvil
Josep Lladós
Alícia Fornés
Anna Cabré
spellingShingle Gunnar Thorvaldsen
Joana Maria Pujadas-Mora
Trygve Andersen
Line Eikvil
Josep Lladós
Alícia Fornés
Anna Cabré
A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
Historical Life Course Studies
Word spotting
Optical Character Recognition
Vital records
Census
Nominative sources
Computer vision
author_facet Gunnar Thorvaldsen
Joana Maria Pujadas-Mora
Trygve Andersen
Line Eikvil
Josep Lladós
Alícia Fornés
Anna Cabré
author_sort Gunnar Thorvaldsen
title A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
title_short A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
title_full A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
title_fullStr A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
title_full_unstemmed A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources
title_sort tale of two transcriptions. machine-assisted transcription of historical sources
publisher International Instititute of Social History
series Historical Life Course Studies
issn 2352-6343
publishDate 2015-01-01
description This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources.
topic Word spotting
Optical Character Recognition
Vital records
Census
Nominative sources
Computer vision
url https://hlcs.nl/article/view/9355
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