MUDIDI Framework Uses Language Models to Digitize Multilingual Dictionaries for Endangered Languages
Researchers introduced MUDIDI, a two-stage framework that uses vision-language models and large language models to convert scanned multilingual dictionaries into machine-readable formats. The system addresses long-standing challenges in digitizing dictionaries with complex layouts, multiple writing systems, and lexicographic structures. This work is significant for preserving endangered languages and making linguistic resources accessible for research and community use.
MUDIDI is a two-stage computational framework designed to digitize multilingual dictionaries that currently exist only as scans. Stage One evaluates character recognition quality and markup preservation, while Stage Two focuses on segmenting dictionary entries and mapping them to a standardized lexicographic schema (SIL's Multi-Dictionary Formatter). The researchers released a dataset of human-annotated entries from 30 public-domain dictionaries spanning diverse writing systems and language families. Benchmarking tests show that large language models outperformed both traditional OCR systems and general-purpose vision-language models across most writing systems. The study also demonstrates that providing additional context—such as dictionary introductions—to LLMs improves digitization quality, offering practical guidance for handling challenging scenarios.
What's missing
The study does not discuss potential limitations in handling extremely rare or undocumented writing systems, computational costs of the two-stage approach, or how performance scales with dictionary size. The paper also does not address potential errors in the human annotation process used to create the benchmark dataset or inter-annotator agreement metrics.
What different sources said
- arXiv cs.CLCenter
MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models
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