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1 – 2 of 2Guenter Muehlberger, Louise Seaward, Melissa Terras, Sofia Ares Oliveira, Vicente Bosch, Maximilian Bryan, Sebastian Colutto, Hervé Déjean, Markus Diem, Stefan Fiel, Basilis Gatos, Albert Greinoecker, Tobias Grüning, Guenter Hackl, Vili Haukkovaara, Gerhard Heyer, Lauri Hirvonen, Tobias Hodel, Matti Jokinen, Philip Kahle, Mario Kallio, Frederic Kaplan, Florian Kleber, Roger Labahn, Eva Maria Lang, Sören Laube, Gundram Leifert, Georgios Louloudis, Rory McNicholl, Jean-Luc Meunier, Johannes Michael, Elena Mühlbauer, Nathanael Philipp, Ioannis Pratikakis, Joan Puigcerver Pérez, Hannelore Putz, George Retsinas, Verónica Romero, Robert Sablatnig, Joan Andreu Sánchez, Philip Schofield, Giorgos Sfikas, Christian Sieber, Nikolaos Stamatopoulos, Tobias Strauß, Tamara Terbul, Alejandro Héctor Toselli, Berthold Ulreich, Mauricio Villegas, Enrique Vidal, Johanna Walcher, Max Weidemann, Herbert Wurster and Konstantinos Zagoris
An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR…
Abstract
Purpose
An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues.
Design/methodology/approach
This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material.
Findings
Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified.
Research limitations/implications
The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc.
Practical implications
Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field.
Social implications
The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals.
Originality/value
This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector.
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Keywords
Sara Lafia, David A. Bleckley and J. Trent Alexander
Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use…
Abstract
Purpose
Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.
Design/methodology/approach
The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.
Findings
The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.
Originality/value
The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.
Details