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Datasets:
Davidsamuel101
/
MUDIDI

Tasks:
Text Generation
Modalities:
Document
Text
Formats:
parquet
Languages:
Multiple languages
Size:
< 1K
ArXiv:
Tags:
dictionary-digitization
ocr
multilingual
mdf
mudidi
lexicography
DOI:
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Datasets
pandas
Polars
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1

Protecting the integrity of the MUDIDI benchmark for evaluation

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

MUDIDI is a gated evaluation benchmark for multilingual dictionary digitization. Gold annotations are released under CC BY-NC 4.0; source page images are derived from public-domain HathiTrust scans. By requesting access you agree to the conditions below.

Log in or Sign Up to review the conditions and access this dataset content.

MUDIDI Dataset

Gold annotations and source pages for MUDIDI, a two-stage benchmark for multilingual dictionary digitization.

Access: This dataset is gated (gated: auto). Log in, open the dataset page, accept the benchmark access conditions (non-commercial use, no crawler-exposed re-hosting, no training-data contamination when evaluating), then authenticate with a Hugging Face token before downloading.

Dataset summary

Property Value
Dictionaries 30 public-domain bilingual dictionaries
Stage 1 gold pages 85 annotated pages across 30 dictionaries (27 complete at 3 pages; Yiddish-English 1, Georgian-Russian 1, Japanese-English 2)
Stage 1 snippet pages 90 source images (3 per dictionary; includes pages without gold yet)
Stage 2 pages 10 (1 representative page per Stage 2 subset dictionary)
Writing systems Latin, Cyrillic, Greek, Devanagari, Bengali, Gujarati, Gurmukhi, Telugu, Hebrew, Syriac, Arabic-based, Khmer, Han, Kana, Cuneiform, IPA, and more
Splits None — evaluation benchmark only (no train/test split)
Version v1.0.0

Repository layout

Top level:

.
├── LICENSE
├── README.md
└── dictionaries/
    └── <Source-Target>/
        ├── dictionary_languages.yaml
        ├── Alphabet list/
        ├── Dictionary pages/
        ├── Stage 1 Gold OCR/
        ├── Stage 2 Gold Cheat Sheet/   # 10 dictionaries only
        └── Stage 2 MDF file/         # 10 dictionaries only

Each dictionary lives under dictionaries/<Source-Target>/:

Path Contents Format
dictionary_languages.yaml Source/target language codes, writing system, HathiTrust archive ID YAML
Alphabet list/ Source-language character inventory (optional hint for Stage 1) alphabet.txt — one character per line
Dictionary pages/ Three sampled dictionary entry pages page_<N>.png (or .pdf, …)
Stage 1 Gold OCR/ Human column-aligned gold + derived flat transcript page_<N>/page_<N>_stage1_GOLD.tsv, page_<N>/page_<N>_stage1_GOLD_flat.txt
Stage 2 Gold Cheat Sheet/ Human MDF marker schema (10 dicts only) field_cheatsheet.json
Stage 2 MDF file/ Human Toolbox MDF records (10 dicts only) page_<N>/page_<N>.mdf.txt

Example (Evenki-Russian):

dictionaries/Evenki-Russian/
├── dictionary_languages.yaml
├── Alphabet list/alphabet.txt
├── Dictionary pages/page_1.png
├── Dictionary pages/page_2.png
├── Dictionary pages/page_3.png
├── Stage 1 Gold OCR/page_1/page_1_stage1_GOLD.tsv
├── Stage 1 Gold OCR/page_1/page_1_stage1_GOLD_flat.txt
├── Stage 2 Gold Cheat Sheet/field_cheatsheet.json
└── Stage 2 MDF file/page_1/page_1.mdf.txt

Some dictionaries may also include an Introduction/ folder (preface pages for Stage 2 field discovery). It is optional and not present in every dictionary in this release.

Stage 1 gold format

Gold files live under Stage 1 Gold OCR/page_<N>/:

  • page_<N>_stage1_GOLD.tsv — column-aligned human annotation (primary gold).
  • page_<N>_stage1_GOLD_flat.txt — derived flat transcript: one line per visible row, column-major reading order, with <b> / <i> markup preserved.

See stage 1 methodology for annotation conventions.

Stage 2 gold format

Stage 2 gold is available for 10 dictionaries only:

  • Evenki-Russian, Chukchi-Russian, Nahuatl-French, Na-English-Chinese-French, Kashmiri-English, Tiri-English, Greek-English, Efik-English, Circassian-English-Turkish, Iñupiatun Eskimo-English

  • field_cheatsheet.json — maps dictionary-specific MDF markers to entry structure rules (Pass 1 gold).

  • page_<N>.mdf.txt — blank-line-delimited SIL Toolbox MDF records (Pass 2 gold).

See stage 2 methodology.

Loading with datasets

Tabular gold text is exported under parquet/<config>/train.parquet (one row per annotated page). Each configuration maps to one bilingual dictionary and exposes a train split with:

Column Source
page_id Page stem, e.g. page_54
ocr_text Stage 1 Gold OCR/page_<N>/page_<N>_stage1_GOLD_flat.txt
mdf_text Stage 2 MDF file/page_<N>/page_<N>.mdf.txt when present, else empty 
from datasets import load_dataset

ds = load_dataset("Davidsamuel101/MUDIDI", "bengalese-english", split="train")
print(ds[0]["page_id"])
print(ds[0]["ocr_text"][:200])
print(ds[0]["mdf_text"])  # empty unless this dictionary has Stage 2 MDF gold

Requires datasets>=3.0. Regenerate Parquet exports after editing gold files with python build_parquet.py in this repository.

Downloading the dataset

Hugging Face (gated)

  1. Open huggingface.co/datasets/Davidsamuel101/MUDIDI while logged in and accept the dataset terms.
  2. Create a token with read access at huggingface.co/settings/tokens.
  3. Authenticate locally:
hf auth login
# or: export HF_TOKEN=hf_...
  1. Download the full dataset:
# into the MUDIDI repo layout used by examples/
hf download Davidsamuel101/MUDIDI --repo-type dataset --local-dir dataset/mudidi

For a custom destination:

hf download Davidsamuel101/MUDIDI --repo-type dataset --local-dir /path/to/mudidi

Large downloads can also use:

hf download Davidsamuel101/MUDIDI --repo-type dataset --local-dir dataset/mudidi --max-workers 8

Raw files (without datasets) 

from pathlib import Path

from huggingface_hub import login, snapshot_download

login()  # or set HF_TOKEN
root = Path(snapshot_download("Davidsamuel101/MUDIDI", repo_type="dataset"))

# Stage 1 evaluation pages (85 gold TSV files)
stage1_pages = sorted(root.glob("dictionaries/*/Stage 1 Gold OCR/page_*/*_stage1_GOLD.tsv"))

# Stage 2 dictionaries (10 with MDF gold)
stage2_dicts = sorted(
    p.parents[1].name
    for p in root.glob("dictionaries/*/Stage 2 Gold Cheat Sheet/field_cheatsheet.json")
)

# Example: Evenki-Russian page_1 paths
evenki = root / "dictionaries/Evenki-Russian"
page_image = evenki / "Dictionary pages/page_1.png"
page_gold = evenki / "Stage 1 Gold OCR/page_1/page_1_stage1_GOLD_flat.txt"
page_mdf = evenki / "Stage 2 MDF file/page_1/page_1.mdf.txt"

From the GitHub repository

Gold data is also vendored under dataset/mudidi/ in the MUDIDI repository. Inference and evaluation examples in that repo expect this layout.

Known limitations

  • Stage 2 gold covers 10 of 30 dictionaries; do not assume MDF gold for all languages.
  • Stage 1 gold is incomplete for Yiddish-English (1/3 pages), Georgian-Russian (1/3), and Japanese-English (2/3). All 90 snippet images are included; gold files exist only for annotated pages under Stage 1 Gold OCR/.
  • Some dictionaries have sparse or missing introduction pages in the source scan; Introduction/ folders are not included in this release.
  • Source PDFs are public-domain scans; typography and scan quality vary.

Licensing

  • Annotations (gold TSV, flat transcripts, MDF, cheat sheets, alphabet lists): CC BY-NC 4.0
  • Source page images: derived from public-domain HathiTrust volumes

See LICENSE for details.

Citation

If you use the dataset:

@misc{mudidi_v1,
  title        = {{MUDIDI: Multilingual Dictionary Digitization Benchmark}},
  author       = {Setiawan, David and Khishigsuren, Temuulen and Agarwal, Milind and Pit, Pagnarith and Mahmudi, Aso and Vylomova, Ekaterina},
  year         = {2026},
  version      = {v1.0.0},
  howpublished = {\url{https://huggingface.co/datasets/Davidsamuel101/MUDIDI}}
}

If you use the framework or report benchmark results from the paper:

@misc{mudidi2026,
  title         = {{MUDIDI: A Two-Stage Framework for Multilingual Dictionary Digitization with Language Models}},
  author        = {Setiawan, David and Khishigsuren, Temuulen and Agarwal, Milind and Pit, Pagnarith and Mahmudi, Aso and Vylomova, Ekaterina},
  year          = {2026},
  eprint        = {2606.09435},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  doi           = {10.48550/arXiv.2606.09435},
  url           = {https://arxiv.org/abs/2606.09435},
  note          = {Submitted to EMNLP 2026}
}
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