MUDIDI introduces a two-stage LLM pipeline for multilingual dictionary digitization, releases a human-annotated dataset from 30 dictionaries, and shows LLMs outperforming OCR and VLMs on character recognition, markup, and entry segmentation.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , year =
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MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models
MUDIDI introduces a two-stage LLM pipeline for multilingual dictionary digitization, releases a human-annotated dataset from 30 dictionaries, and shows LLMs outperforming OCR and VLMs on character recognition, markup, and entry segmentation.