Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2023

Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done

Tobias Hodel
Helle Strandgaard Jensen
Andy Stauder
Melissa Terras
Achim Rabus
Katrien Depuydt
Dorothee Huff
Joe Nockels
Laura Noort
  • Function : Author
Joost Johannes Oosterhuis
Vivien Popken
  • Function : Author
María Estrella Puertollano
  • Function : Author
Joosep Puusaag
  • Function : Author
Ahmed Sheta
  • Function : Author
Lex Stoop
  • Function : Author
Ebba Strutzenbladh
  • Function : Author
Nicoline van Der Sijs
Jan Paul van Der Spek
  • Function : Author
Barry Benaissa Trouw
  • Function : Author
Geertrui van Synghel
  • Function : Author
Vladimir Vučković
  • Function : Author
Heleen Wilbrink
  • Function : Author
Sonia Weiss
  • Function : Author
David Joseph Wrisley
Riet Zweistra
  • Function : Author

Abstract

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, as well as ways to reference and acknowledge contributions to the creation and enrichment of data within these systems. We discuss how one can place Ground Truth data in a repository and, subsequently, inform others through HTR-United. Furthermore, we want to suggest appropriate citation methods for ATR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of machine learning in archival and library contexts, and how the community should begin to acknowledge and record both contributions and data provenance.
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hal-04244372 , version 1 (16-10-2023)

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C. Annemieke Romein, Tobias Hodel, Femke Gordijn, Joris Zundert, Alix Chagué, et al.. Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done. 2023. ⟨hal-04244372⟩
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