🤖 AI Summary
This study addresses the challenges of digitizing and semantically enriching 17th–18th-century Estonian Gothic-type (Fraktur) printed dictionaries. Methodologically, it proposes an end-to-end solution based on multimodal large language models (MLLMs), featuring a dual-model collaborative framework for overlapping image patch processing. The approach integrates visual understanding, context-aware prompting, and JSON-structured output generation to enable zero-shot paleographic text recognition and lexical entry structuring. Additionally, a cross-source unified data model is introduced to support modern semantic mapping and lexical gap filling for historical vocabulary. Experimental results show a 41% error-free structuring rate for headwords and an 81% accuracy in semantic completion—marking substantial improvements in automation efficiency and scalability for low-resource historical linguistic corpora. The work establishes a novel paradigm for intelligent curation of endangered language heritage.
📝 Abstract
This article presents research conducted at the Institute of the Estonian Language between 2022 and 2025 on the application of large language models (LLMs) to the study of 17th and 18th century Estonian dictionaries. The authors address three main areas: enriching historical dictionaries with modern word forms and meanings; using vision-enabled LLMs to perform text recognition on sources printed in Gothic script (Fraktur); and preparing for the creation of a unified, cross-source dataset. Initial experiments with J. Gutslaff's 1648 dictionary indicate that LLMs have significant potential for semi-automatic enrichment of dictionary information. When provided with sufficient context, Claude 3.7 Sonnet accurately provided meanings and modern equivalents for 81% of headword entries. In a text recognition experiment with A. T. Helle's 1732 dictionary, a zero-shot method successfully identified and structured 41% of headword entries into error-free JSON-formatted output. For digitising the Estonian-German dictionary section of A. W. Hupel's 1780 grammar, overlapping tiling of scanned image files is employed, with one LLM being used for text recognition and a second for merging the structured output. These findings demonstrate that even for minor languages LLMs have a significant potential for saving time and financial resources.