The Ground Truth Gradient: When AI-Assisted Transcription Becomes Historical Record

The use of AI in historical research is bringing new tools and opportunities, as well as complex methodological and ethical challenges. In a pair of blog posts, two IHR MA students reflect on their experiences using AI in their research. In this first post, Julian Sproule traces how a Digital Humanities seminar inspired a verification protocol using language and text analysis models, examines how individual researchers and institutions arrived at similar methodologies, and explores what this convergence means for developing sector-wide standards. Julian Sproule recently completed an MA in History, Place and Community at the IHR, graduating with distinction.

Can we trust AI to transcribe historical documents? After six months developing verification protocols for AI-assisted transcription of two historical memoirs, I recently attended the Digital Humanities Responsible AI and Cultural Heritage Forum seeking answers. When I asked The National Archives about ‘ground truth’ status for their AI transcriptions, I discovered we had arrived at the same conclusion independently: authority in AI transcription isn’t binary but exists on a gradient, built through layers of systematic verification. This convergence between my practical necessity and their institutional planning reveals how the field is developing new frameworks for AI-assisted historical research.

This article traces how a May 2024 Digital Humanities seminar inspired a verification protocol using language and text analysis models, tested across two separate ego-documents: a 16th-century memoir by a Brabantian merchant in London (verified through his will), and an unrelated 19th-century memoir by an Italian noblewoman. It examines how individual researchers and institutions arrived at similar methodologies, and what this convergence means for developing sector-wide standards.

From Panel to Practice

My engagement with AI-assisted transcription began at a Digital Humanities Research Hub panel in May 2024, where discussion of projects like HathiTrust suggested approaches to digital textual studies’ response to AI developments. The panel emphasised raising non-Anglophone DH research visibility whilst maintaining rigorous syntactic analysis—textual study remaining fundamental to humanities scholarship despite technological innovation. Discussion of language and text analysis models suggested an approach to the multilingual sources I was encountering.

This framework proved essential when I encountered two unrelated ego-documents during my MA research at the Institute of Historical Research. The first was a 16th-century memoir by a Brabantian merchant living in Elizabethan London—a handwritten manuscript requiring palaeographic expertise for early modern Dutch. The second, entirely separate, was a 90,000-word memoir by an Italian noblewoman spanning European aristocratic life. Written between 1841-1940 but surviving only as a deteriorated 1920s French typescript, it included passages in Italian, Piedmontese, Spanish, Latin, English, and German.

Professional translation services would have made the research impossible. Drawing on the panel’s discussion, I developed an AI-assisted methodology maintaining scholarly standards. The six-month iterative process produced a practice-based approach—developed through necessity rather than formal training—but one unexpectedly aligned with institutional thinking.

The Transkribus Test Case

I tested these tools on two distinct sets of documents. For the Brabantian merchant, authentication of his 16th-century memoir required verification against contemporary sources. I located his will at The National Archives (PROB 11/57/29, proved 1575) and used Transkribus to transcribe the secretary hand traditionally requiring palaeographic expertise. The will’s details—wife’s name, daughters’ names, London address, merchant family connections—matched references in his memoir, confirming its authenticity.

The Italian noblewoman’s memoir presented different challenges entirely. This separate 19th-century document survived only as deteriorated typescript. Initial attempts with both memoirs failed repeatedly: standard OCR couldn’t process the noblewoman’s deteriorated pages, Google Translate produced nonsensical period idioms from both texts, and automated translation of the merchant’s early modern Dutch memoir proved impossible until native speakers provided contextual understanding. At the November 2025 forum, The National Archives and other participants confirmed they were implementing similar protocols, addressing the fundamental question: When does AI-assisted transcription achieve authoritative status?

Developing Verification Protocols

My methodology evolved through iterative testing of both documents, moving from single-point authority to convergent validation:

• Layer 1 – Initial Transcription – Transkribus for the 16th-century merchant’s will; iPad OCR for the noblewoman’s deteriorated typescript; neural machine translation (Word/Google) for first-pass translations.
• Layer 2 – Linguistic Verification – Native speakers checked period-appropriate language, preserved authorial voice, and flagged obsolete expressions and idioms.
• Layer 3 – Syntactic Analysis – Voyant Tools supplied statistical text analysis, comparing word-frequency patterns to ensure thematic and semantic consistency.
• Layer 4 – External Validation – Cross-referencing with Dutch, Belgian, Italian, and French archives confirmed proper names, dates, and locations.
• Layer 5 – Scholarly Assessment – Final reliability rating, documented uncertainties, and transparent methodology reporting.

Understanding the Technology: Large Language Models in Practice

The consumer tools employed operate through distinct large language model architectures. Microsoft Word’s translation utilises Azure Cognitive Services, representing a shift from rule-based translation to probabilistic language modelling.

For historical texts—whether the merchant’s 16th-century manuscript or the noblewoman’s 19th-century typescript—this creates specific challenges. LLMs train predominantly on contemporary corpora, creating systematic blind spots around period language and historical context. Beyond blind spots, LLMs generate ‘false friends’—translations appearing linguistically plausible but misinterpreting period-specific meanings. The merchant’s early modern commercial terminology might be rendered in contemporary business language, obscuring original historical context. The noblewoman’s 19th-century aristocratic vocabulary required careful verification to avoid modernising translations that would distort her social world. Native speakers identified not just errors, but seemingly correct translations that subtly distorted meaning.

My methodology leveraged multiple LLMs comparatively: when Word’s Azure-based system and Google’s neural translation converged, confidence increased. Divergence indicated passages requiring specialist verification. This approach treats LLMs as first-pass processors requiring human oversight rather than authoritative translators.

Understanding these technological underpinnings—discussed at the November 2025 forum—proves essential for developing responsible methodologies. The forum emphasised humanities practitioners face generational technical change requiring critical understanding rather than fear. Knowing why LLMs struggle with historical text informs verification, enabling appropriate deployment.

Institutional Implications

The November 2025 forum revealed the heritage sector was examining and restructuring its approach to digital records in ways paralleling my methodology. This convergence has implications for deploying AI tools in historical research.

Key considerations from institutional planning and individual practice:

• Data Integrity: AI transcription requires multiple verification layers to approach traditional scholarly standards. Hybrid approaches yield superior results to pure automation or manual transcription.
• Accessibility vs Authority: Democratising access through AI must not compromise scholarly rigour. This methodology makes multilingual research feasible for independent scholars whilst maintaining verification standards.
• Documentation Standards: Transparent reporting of AI assistance, verification methods, and confidence levels becomes essential. ‘AI-assisted’ requires precise definition—which tools, what verification, which human interventions.

The sector is developing standards for maintaining derivative independent research forming the foundation of historical scholarship. AI becomes a measured, ethical process requiring accurate, verified sources.

The Ground Truth Gradient

My exchange with The National Archives crystallised a crucial insight: ground truth in AI-assisted transcription operates as a gradient rather than binary state. When I asked about their testing of Transkribus transcriptions for ‘ground truth’ status, their response revealed they were developing similar verification protocols, recognising authority must be earned through systematic validation.

Each verification layer increases confidence without achieving absolute certainty. This parallels traditional palaeographic practice, where expert transcriptions carry authority through accumulated expertise rather than infallibility. For institutional collections, this suggests confidence thresholds rather than certified/uncertified binaries. A Transkribus transcription with native speaker verification and archival cross-referencing might achieve ‘research-grade’ status without claiming definitive authority. Individual researchers can adapt these gradients to their requirements and resources.

Conclusion

The ground truth question revealed shared challenges across individual and institutional practice. My work on these two separate memoirs—the Brabantian merchant’s and the Italian noblewoman’s—developed protocols balancing accessibility with rigour. Convergence with institutional approaches suggests transition: what began through practical necessity proved valid, aligning with methodologies being developed by heritage institutions. The sector is formalising frameworks providing students and academics with established standards rather than requiring ad-hoc solutions.

The six-month methodology development demonstrates that whilst AI tools can democratise access to multilingual historical sources, they require critical engagement comparable to any historical method. The November 2025 forum’s emphasis on critical understanding finds expression in methodologies treating AI as measured process with verified sources—not convenience tool but instrument requiring rigorous protocols.

As the heritage sector develops training for responsible AI implementation, individual approaches may become redundant—replaced by established protocols enabling derivative independent research without reinventing verification methods. The School of Advanced Study prepares students for AI ethics through training such as ‘The Risks and Benefits of Using AI in Research’, organised by Kremena Velinova, Head of Research Training. Professor Susan Breau from the Institute of Advanced Legal Studies emphasised current practice and policy guidance, demonstrating institutional commitment to equipping researchers with necessary frameworks. This represents progress: shift from experimentation to sector-wide standards maintaining scholarly rigour whilst democratising access.

Establishing AI’s role in historical scholarship requires both institutional frameworks and practical experience from researchers who confronted these questions through necessity. That November 2025 exchange about the ground truth gradient crystallised this dialogue between practice and emerging standards.

For those interested in observing development underlying AI, HuggingFace offers insight into rapid changes shaping these technologies—a reminder that as standards emerge, the field continues evolving.

Julian Sproule completed an MA in History, Place and Community at the IHR, graduating with distinction in 2025. His research focuses on ego-documents and recovering marginalized voices from historical archives, with particular expertise in multilingual sources across French, Italian, and Spanish. Before turning to academic research he worked in financial services in London and co-founded ventures in renewable energy storage. His interest in rigorous methodology stems from both his academic training and experience in fields requiring systematic verification. He has participated in science-based expeditions to remote regions and served on the advisory board of Columbia University’s Lamont-Doherty Earth Observatory.

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