Introduction

Jaillant, Lise

doi:10.1515/9783839455845-001

Cited by 2 publications

(1 citation statement)

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“…Without this, there is a real risk that HTR outputs only serve to cement hegemonic archival structures. Well-researched and documented collections which have been previously digitised will return stronger models, given they provide extensive training data: “accordingly, the resulting models are highly biased by the material they are trained on” (Hodel, 2022, p. 171). Depending on the training data, HTR can voice colonial artefacts (Stoler, 2008), prioritising the coloniser and not the colonised.…”

Section: Resultsmentioning

confidence: 99%

The implications of handwritten text recognition for accessing the past at scale

Nockels,

Gooding,

Terras

2024

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PurposeThis paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.Design/methodology/approachIn undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.FindingsCatalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.Originality/valueOur research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

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Section: Resultsmentioning

confidence: 99%

The implications of handwritten text recognition for accessing the past at scale

Nockels,

Gooding,

Terras

2024

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Towards privacy-aware exploration of archived personal emails

Bartliff,

Kim,

Hopfgartner

2024

Int J Digit Libr

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This paper examines how privacy measures, such as anonymisation and aggregation processes for email collections, can affect the perceived usefulness of email visualisations for research, especially in the humanities and social sciences. The work is intended to inform archivists and data managers who are faced with the challenge of accessioning and reviewing increasingly sizeable and complex personal digital collections. The research in this paper provides a focused user study to investigate the usefulness of data visualisation as a mediator between privacy-aware management of data and maximisation of research value of data. The research is carried out with researchers and archivists with vested interest in using, making sense of, and/or archiving the data to derive meaningful results. Participants tend to perceive email visualisations as useful, with an average rating of 4.281 (out of 7) for all the visualisations in the study, with above average ratings for mountain graphs and word trees. The study shows that while participants voice a strong desire for information identifying individuals in email data, they perceive visualisations as almost equally useful for their research and/or work when aggregation is employed in addition to anonymisation.

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Introduction

Cited by 2 publications

References 0 publications

The implications of handwritten text recognition for accessing the past at scale

The implications of handwritten text recognition for accessing the past at scale

Towards privacy-aware exploration of archived personal emails

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