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Reading the ransom: Methodological advancements in extracting the Swedish Wealth Tax of 1571
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- Blomqvist, Christopher (author)
- Lund University
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- Enflo, Kerstin (author)
- Lund University,Lunds universitet,Tillväxt, teknologisk förändring och ojämlikhet,Ekonomisk-historiska institutionen,Ekonomihögskolan,Growth, technological change, and inequality,Department of Economic History,Lund University School of Economics and Management, LUSEM
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- Jakobsson, Andreas (author)
- Lund University,Lunds universitet,Biomedical Modelling and Computation,Forskargrupper vid Lunds universitet,Statistical Signal Processing Group,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,LTH profilområde: Teknik för hälsa,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,LTH Profile Area: Engineering Health,Faculty of Engineering, LTH
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- (creator_code:org_t)
- Elsevier BV, 2023
- 2023
- English.
- In: Explorations in Economic History. - : Elsevier BV. - 0014-4983. ; 87
- Journal article (peer-reviewed)
Abstract
Subject headings
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- We describe a deep learning method to read hand-written records from the 16th century. The method consists of a combination of a segmentation module and a Handwritten Text Recognition (HTR) module. The transformer-based HTR module exploits both language and image features in reading, classifying and extracting the position of each word on the page. The method is demonstrated on a unique historical document: The Swedish Wealth Tax of 1571. Results suggest that the segmentation module performs significantly better than the lay-out analysis implemented in state-of-the art programs, enabling us to trace many more text blocks correctly on each page. The HTR module has a low character error rate (CER), in addition to being able to classify words and help organize them into tabular formats. By demonstrating an automated process to transform loosely structured handwritten information from the 16th century into organized tables, our method should interest economic historians seeking to digitize and organize quantitative material from pre-industrial periods.
Subject headings
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv -- Ekonomisk historia (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business -- Economic History (hsv//eng)
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- art (subject category)
- ref (subject category)
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