http://hdl.handle.net/2451/42232 Thu, 16 Jul 2026 23:23:10 GMT 2026-07-16T23:23:10Z http://hdl.handle.net/2451/75857 Title: Transcribing Machines: HTR as a Question of Critical AI Authors: Wrisley, David Joseph Abstract: This talk examines Handwritten Text Recognition (HTR) not as a neutral tool of transcription, but as a site where salient debates of Critical AI emerge. HTR, it has been argued, is perhaps the most successful form of AI introduced into the archive (Neudecker). Yet, while it promises scaled access to the handwritten unread, it also unsettles traditional scholarly authority, raising fundamental questions about who produces and standardizes the creation of searchable text. Drawing on multilingual, multi-community experiences with HTR, I argue that while these systems open up important critical perspectives in the computational study of texts, they also reshape authority and expertise under which documentary knowledge is produced. One of the central questions of this talk is whether the humanities community is treating automated transcription too simply as a technical problem, while overlooking forms of extraction hidden beneath promises of access. Training corpora—often drawn from wealthy institutions of the Global North—privilege certain scripts, languages, and archival traditions, making the question of whose data and models are centered a critical one. As HTR transforms select handwritten materials into searchable corpora–readable by both humans and machines–it also runs the risk of deepening the gap between well-resourced textual traditions and those whose scripts or archival conditions remain difficult to computationally process. These inequalities are not merely technical, but they shape the conditions under which the past can be discovered and known. Taken together, these tensions point toward a larger question about the future of HTR on which we can only speculate: will it remain shaped by scholarly commitments to transparency and context, or increasingly by AI infrastructures designed for scalability and generalized results? Automation may eliminate some older scholarly practices (Dubreuil) such as manual transcription, but it may also compel the humanities to focus more strongly on critical questions of systems, infrastructure, and methodological inclusivity. Fri, 12 Jun 2026 00:00:00 GMT http://hdl.handle.net/2451/75857 2026-06-12T00:00:00Z http://hdl.handle.net/2451/75614 Title: Medieval Manuscripts and the Computational Humanities Big Data, Scribes, and the “Paris Bible” Authors: Wrisley, David Joseph; Guéville, Estelle Abstract: This book examines the transformations in medieval studies—and the humanities more broadly—enabled by decades of digitization and advances in computational methods. Centring on the Paris Bible, a widely copied thirteenth- and fourteenth-century manuscript genre, we demonstrate how automated transcription produces scribal data at a scale once inaccessible, and how automation can support new approaches to localizing, dating, and contextualizing manuscripts. We argue that bringing machine learning and artificial intelligence to medieval studies not only requires re-centring expert human intelligence within computational systems, but also raises the question of the infrastructures needed for equitable, collaborative scholarship across the field. The book models how medieval studies might rethink interpretation, highlighting both the promise and risks of computational methods in manuscript research. Sun, 01 Feb 2026 00:00:00 GMT http://hdl.handle.net/2451/75614 2026-02-01T00:00:00Z http://hdl.handle.net/2451/75159 Title: AI and Digital Humanities in the Arabian Gulf: Interdisciplinary Perspectives on Infrastructure, Cultural Heritage, and Community Building Authors: Catelan, Nicolas; Fresquet, Xavier; Moura, Sabrina; Svard, Proscovia; Wrisley, David Joseph Abstract: This article examines the integration of artificial intelligence (AI) in digital humanities and cultural heritage preservation across the Arabian Gulf region. It highlights the ethical, legal, and community-centered challenges raised by AI in archives, museums, and libraries, while showcasing local initiatives that adopt inclusive and culturally grounded approaches. The paper calls for an interdisciplinary governance of AI, anchored in shared infrastructures, context-sensitive regulation, and active community participation. Tue, 13 May 2025 00:00:00 GMT http://hdl.handle.net/2451/75159 2025-05-13T00:00:00Z http://hdl.handle.net/2451/74851 Title: Everyone Leaves a Trace: Exploring Transcriptions of Medieval Manuscripts with Computational Methods Authors: Guéville, Estelle; Wrisley, David Joseph Abstract: The topic of this paper is a thirteenth-century manuscript from the French National Library (Paris, BnF français 24428) containing three popular texts: an encyclopedic work, a bestiary and a collection of animal fables. We have automatically transcribed the manuscript using a custom handwritten text recognition (HTR) model for old French. Rather than a content-based analysis of the manuscript’s transcription, we adapt quantitative methods normally used for authorship attribution and clustering to the analysis of scribal contribution in the manuscript. Furthermore, we explore the traces that are left when texts are copied, transcribed and/or edited, and the importance of that trace for computational textual analysis with orthographically unstable historical languages. We argue that the method of transcription is fundamental for being able to think about complex modes of authorship which are so important for understanding medieval textual transmission. The paper is inspired by trends in digital scholarship in the mid-2020s, such as public transcribe-a-thons in the GLAM (Galleries, Libraries, Archives and Museums) sector, the opening up of digitized archival collections with methods such as HTR, and computational textual analysis of the transcriptions. Fri, 29 Nov 2024 00:00:00 GMT http://hdl.handle.net/2451/74851 2024-11-29T00:00:00Z