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

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

Transcribing "Le Pèlerinage de Damoiselle Sapience": Scholarly Editing Covid19-Style

This article describes a methodological experiment conducted during the 13th Annual (Virtual) Schoenberg Symposium on Manuscript Studies in the Digital Age, hosted by the University of Pennsylvania, November 18–20, 2020. The experiment consisted of a “relay style” event in which three teams transcribed, revised, and prepared for submission to this journal a full edition of the “Le Pèlerinage de Damoiselle Sapience” and other texts from UPenn Ms Codex 660, ff. 86r–95v within the three-day timespan of the conference. The project used methods typical of crowdsourcing and drew participants from all over the world and from all different stages of their careers. After one group completed its work, the results were passed into the hands of the next. The final result—in the form of a finished manuscript edition, ready for submission to Digital Medievalist—was presented on the last day of the conference. The main purpose of this experiment was to demonstrate how the work of the transcriber and editor might be structured as a short-term digital event that relied wholly on virtual interactions with both the source materials and among collaborators. This method also reveals the positive aspects of the many challenges posed by working simultaneously, remotely, and globally

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

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