Optimisation of archival processes involving digitisation of typewritten documents

Stančić, Hrvoje; Trbušić, Željko

doi:10.1108/ajim-11-2019-0326

Cited by 3 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Omitting layout contexts from the processing of paper-based collections results in flat, unstructured text. Even if the OCR output text is high quality, it still requires substantial manual processing to delineate separate fields for record linkage and analysis, which are necessary precursors for research use (Stančić and Trbušić, 2020). Manual processes are often the main bottlenecks in digitization workflows (Blanke et al ., 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Much of the historical documentation OCR literature focuses on the digitization of prose documents or the conversion of hard copy tabular records into digital tabular data (Nagy, 1992; Stančić and Trbušić, 2020). However, bridging these use cases is the digitization of semi-structured historical documents, which hold data that could be converted into a tabular format but are not currently formatted into forms or tables.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digitizing and parsing semi-structured historical administrative documents from the G.I. Bill mortgage guarantee program

Lafia,

Bleckley,

Alexander

2023

View full text Add to dashboard Cite

PurposeMany 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/approachThe 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.FindingsThe 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/valueThe 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digitizing and parsing semi-structured historical administrative documents from the G.I. Bill mortgage guarantee program

Lafia,

Bleckley,

Alexander

2023

View full text Add to dashboard Cite

show abstract

“…Digitization can be defined as the action of moving physical elements and analogous methods to the digital plane, which involves the consideration of the use of data warehouses or the scanning of paper files to record all relevant documents, discarding outdated filing cabinets. It may be implemented as a system at the service of the informative, investigative and managerial legacy of any organization since it facilitates the supervision of a volume of information, as well as its editing and management [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Fields Touched by Digitalization: Analysis of Scientific Activity in Scopus

Espina-Romero

Alcedo²

2022

Sustainability

View full text Add to dashboard Cite

This study aims to analyze the publications in Scopus around digitalization in the space of time between 2018 and 2022. A bibliometric review is carried out with a bibliographic approach for 658 documents, which were processed by RStudio and VOSviewer software. The findings show the ten fields where digitization is most applied: “Archives, Corruption and Economy”, “Industry 4.0, Internet of Things, Sustainability and Big Data”, “Cultural Heritage, Deep Learning, Preservation and BIM”, “Photogrammetry and 3D Digitalization”, “Artificial Intelligence (AI) and Supply Chain Management”, “Augmented Reality, Machine Learning and Virtual Reality”, “Innovation, Business Model and Publishing Industry”, “Algorithms, E-government and Biometrics”, “Digital Collections” and “Healthcare”. It should be noted that this document is based on 88.14% original studies, validating the results obtained, and it is also one of the most updated studies.

show abstract

Multimodal archive resources organization based on deep learning: a prospective framework

Zhou,

Zhang,

Wang

et al. 2024

AJIM

View full text Add to dashboard Cite

PurposeThe digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.Design/methodology/approachVia a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.FindingsThe authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.Originality/valueThis study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

show abstract

Optimisation of archival processes involving digitisation of typewritten documents

Cited by 3 publications

References 9 publications

Digitizing and parsing semi-structured historical administrative documents from the G.I. Bill mortgage guarantee program

Digitizing and parsing semi-structured historical administrative documents from the G.I. Bill mortgage guarantee program

Fields Touched by Digitalization: Analysis of Scientific Activity in Scopus

Multimodal archive resources organization based on deep learning: a prospective framework

Contact Info

Product

Resources

About