🤖 AI Summary
Medieval Latin legal manuscripts pose significant transcription challenges due to pervasive abbreviations, degraded paleographic features, and complex juridical context, resulting in high error rates. To address this, we propose a four-stage hybrid workflow: (1) initial transcription via a domain-specific handwritten text recognition (HTR) model; (2) image-text joint post-correction using multimodal large language models (MLLMs); (3) prompt-engineered Latin abbreviation expansion; and (4) integration of a named entity consolidation (NEC) module. A key contribution is the construction of “clean ground-truth” training data, substantially enhancing model capacity for medieval linguistic patterns and legal terminology. Evaluated on an academic-grade ground-truth benchmark, our method achieves word error rates (WER) of 2%–7%. Case studies confirm that outputs preserve semantic fidelity while yielding structured, analysis-ready text suitable for historical semantics and digital humanities research—thereby markedly improving both transcription accuracy and scholarly utility of medieval legal documents.
📝 Abstract
This article presents and validates an ideal, four-stage workflow for the high-accuracy transcription and analysis of challenging medieval legal documents. The process begins with a specialized Handwritten Text Recognition (HTR) model, itself created using a novel "Clean Ground Truth" curation method where a Large Language Model (LLM) refines the training data. This HTR model provides a robust baseline transcription (Stage 1). In Stage 2, this baseline is fed, along with the original document image, to an LLM for multimodal post-correction, grounding the LLM's analysis and improving accuracy. The corrected, abbreviated text is then expanded into full, scholarly Latin using a prompt-guided LLM (Stage 3). A final LLM pass performs Named-Entity Correction (NEC), regularizing proper nouns and generating plausible alternatives for ambiguous readings (Stage 4). We validate this workflow through detailed case studies, achieving Word Error Rates (WER) in the range of 2-7% against scholarly ground truths. The results demonstrate that this hybrid, multi-stage approach effectively automates the most laborious aspects of transcription while producing a high-quality, analyzable output, representing a powerful and practical solution for the current technological landscape.