Datasets:

openpecha
/

uchen_ume_classification_dataset

language:
  - bo
license: cc0-1.0
task_categories:
  - image-classification
tags:
  - tibetan
  - uchen
  - ume
  - script-classification
  - paleography
  - buddhist-manuscripts
  - BDRC
pretty_name: Uchen-Ume Classification Benchmark
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: id
      dtype: string
    - name: image_bytes
      dtype: image
    - name: script
      dtype:
        class_label:
          names:
            '0': uchen
            '1': ume
  splits:
    - name: train
      num_bytes: 5599672929
      num_examples: 9110
    - name: validation
      num_bytes: 645878888
      num_examples: 1000
    - name: test
      num_bytes: 567916938
      num_examples: 851
  download_size: 6813558825
  dataset_size: 6813468755

Uchen–Ume Classification Benchmark

A binary image classification dataset for distinguishing two fundamental categories of Tibetan script: Uchen (དབུ་ཅན།, headed script with a horizontal top stroke) and Ume (དབུ་མེད།, headless script without a top stroke). All images are raw, unprocessed manuscript scans from the Buddhist Digital Resource Center (BDRC).

Model: openpecha/uchen-ume-classifier

Dataset summary

Split	Examples	Uchen	Ume
Train	9,110	~3,124	~5,986
Validation	1,000	~340	~660
Test	851	~290	~561
Total	10,961	~3,754	~7,207

Format

Three Parquet files, each with three columns:

Column	Type	Description
`id`	string	Original filename (e.g., `W00KG09391-I00KG093950005.jpg`)
`image_bytes`	image	Raw manuscript scan, unprocessed (no resizing, cropping, or normalization)
`script`	ClassLabel	`uchen` (0) or `ume` (1)

Images are stored in their original resolution and aspect ratio (typically 5:1 landscape pecha format). Any preprocessing (center cropping, patching, CLAHE normalization) should be applied at training or inference time, not stored in the dataset. This makes the dataset maximally reusable across different experimental setups.

Loading the dataset

from datasets import load_dataset

repo = "openpecha/uchen-ume-classification-benchmark"

train = load_dataset(repo, split="train")
val   = load_dataset(repo, split="validation")
test  = load_dataset(repo, split="test")

# Access a sample
sample = train[0]
print(sample["id"])           # "W00KG09391-I00KG093950005.jpg"
print(sample["script"])       # 0 (uchen) or 1 (ume)
sample["image_bytes"].show()  # displays the image

Split methodology

Splits are stratified by class and partitioned at the work level to prevent data leakage. Each filename follows the pattern {work_id}-{image_id}.jpg (e.g., W00KG09391-I00KG093950005.jpg, where W00KG09391 is the work ID). All pages from the same work (manuscript or volume) are assigned to exactly one split — no work appears in more than one of train, validation, or test. This ensures the model cannot exploit visual characteristics shared across pages of the same manuscript (paper tone, ink style, scanning conditions) to inflate evaluation scores.

Image source

All images are digitised manuscript scans from the Buddhist Digital Resource Center (BDRC), encompassing a wide range of Tibetan Buddhist texts and collections. The scans cover diverse conditions: aged paper, modern reprints, varying ink densities, different scanning equipment, and multiple centuries of manuscript production.

Annotation process

Images were annotated through a structured process developed by Dharmaduta in collaboration with BDRC for the BDRC Etext Corpus project, funded by the Khyentse Foundation:

Annotation guidelines were developed based on a multi-year typology of Tibetan scripts by Pentsok Rtsang, defining the visual criteria for Uchen (horizontal head stroke present) and Ume (head stroke absent) classification.
Label mapping: Images originally annotated as uchen_sugthung, uchen_sugdring, or uchen_sugring are labeled uchen. All other Tibetan script subcategories (petsuk, peri, tsegdrig, drudring, druring, druthung, drathung, khyuyig, tsumachug, yigchung, tsugchung, trinyig, dhumri, and others) are labeled ume. Non-script categories (difficult, multi_scripts, non_tibetan) are excluded.
Quality control: Ambiguous images were reviewed by multiple annotators.

Additional files

File	Description
`splits/train_val_test_splits.json`	Full manifest with page IDs, image URLs, and split assignments
`benchmark/benchmark_holdout.json`	60 holdout pages with image URLs, excluded from all splits

License

This dataset is released under CC0 1.0 Universal (Public Domain). The manuscript images are provided by BDRC for research and preservation purposes.

Citation

@misc{karma2026uchenume,
    title        = {Uchen-Ume Classification Benchmark: A Binary Script Classification Dataset for Tibetan Manuscripts},
    author       = {Karma Tashi and Elie Roux},
    year         = {2026},
    publisher    = {HuggingFace},
    url          = {https://huggingface.co/datasets/openpecha/uchen-ume-classification-benchmark},
    note         = {Funded by Khyentse Foundation. Images sourced from the Buddhist Digital Resource Center (BDRC).}
}

Acknowledgements

This dataset was developed by Dharmaduta from specifications provided by the Buddhist Digital Resource Center (BDRC) for the BDRC Etext Corpus, with funding from the Khyentse Foundation. The annotation guidelines are based on a typology of Tibetan scripts by Pentsok Rtsang.