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llm-jp/JSSODa-test

210 downloads in 30 days1 likesLast modified 24 May 2026View on Hugging Face

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image-to-text

Citation text from the card

@inproceedings{sasagawa-etal-2026-evaluating,
  title = {Evaluating Multimodal Large Language Models on Vertically Written Japanese Text},
  author = {Sasagawa, Keito and Kurita, Shuhei and Kawahara, Daisuke},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
  month = {May},
  year = {2026},
  pages = {9065--9081},
  address = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  editor = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio},
  doi = {10.63317/4kgkk6mqmtwg},
  abstract = {Multimodal Large Language Models (MLLMs) have seen rapid advances in recent years and are now being applied to visual document understanding tasks. They are expected to process a wide range of document images across languages, including Japanese. Understanding documents from images requires models to read what are written in them. Since some Japanese documents are written vertically, support for vertical writing is essential. However, research specifically focused on vertically written Japanese text remains limited. In this study, we evaluate the reading capability of existing MLLMs on vertically written Japanese text. First, we generate a synthetic Japanese OCR dataset by rendering Japanese texts into images, and use it for both model fine-tuning and evaluation. This dataset includes Japanese text in both horizontal and vertical writing. We als

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dataset_licence({ "id": "llm-jp/JSSODa-test" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0