Datasets:

BDRC
/

danyig-pedri-binary-script-classifier

license: mit
task_categories:
  - image-classification
tags:
  - tibetan
  - manuscript
  - script-classification
  - bdrc
  - danyig
  - pedri
  - binary
pretty_name: Danyig vs Pedri Binary Script Classification
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: id
      dtype: string
    - name: image_bytes
      dtype: image
    - name: script
      dtype:
        class_label:
          names:
            '0': Danyig
            '1': Pedri
    - name: script_type
      dtype: string
  splits:
    - name: train
      num_bytes: 530900000
      num_examples: 960
    - name: validation
      num_bytes: 59500000
      num_examples: 120
    - name: test
      num_bytes: 79900000
      num_examples: 120
  download_size: 670300000
  dataset_size: 670300000
configs:
  - config_name: default
    data_files:
      - split: train
        path: train-*-of-*.parquet
      - split: validation
        path: val-*-of-*.parquet
      - split: test
        path: test-*-of-*.parquet

Danyig vs Pedri Binary Script Classification Dataset

Stage-2 binary classifier for distinguishing Danyig (5 subscripts: DraDring, DraRing, Drathung, Gongshabma, Tsegdrig) from Pedri (2 subscripts: Peri, Petsuk). Real-only, all images human-reviewed.

Images per class

Class	train	val	test	All
Danyig	480	60	60	600
Pedri	480	60	60	600
Total	960	120	120	1,200

Splits

Manuscript-stratified split — each manuscript work appears in exactly one of train / val / test (no data leakage across splits).

Split	Images	Works
train	960	555
validation	120	12
test	120	116
Total	1,200

Page-level split manifest: splits/pedri-danyig_combined.json.

Parquet schema

Column	Type	Description
`id`	string	BDRC page id (e.g. `W3CN502-I3CN212840005`)
`image_bytes`	binary	JPEG/PNG/TIF page image
`script`	string	`Danyig` or `Pedri`
`script_type`	string	Subscript name (e.g. `Tsegdrig`, `Petsuk`)

See split_stats.json and split_stats.md for row-level counts.

Load in Python

from datasets import load_dataset

ds = load_dataset("BDRC/danyig-pedri-binary-script-classifier")
train = ds["train"]       # 960
val   = ds["validation"]  # 120
test  = ds["test"]        # 120

from io import BytesIO
from PIL import Image

row = train[0]
img = Image.open(BytesIO(row["image_bytes"])).convert("RGB")
print(row["id"], row["script"])

Citation

@misc{bdrc_danyig_pedri_binary,
  title  = {Danyig vs Pedri Binary Script Classification Dataset},
  author = {Buddhist Digital Resource Center and OpenPecha},
  year   = {2026},
  url    = {https://huggingface.co/datasets/BDRC/danyig-pedri-binary-script-classifier},
  note   = {Images from BDRC}
}

License

Images taken from the open access collection of the Buddhist Digital Resource Center. Not all images are in the public domain, some are from recent publications possibly under copyright. We provide the images under the Fair Use copyright exception, but any reuse of this dataset will have to be based on a copyright analysis. We provide the classification data under the CC0 1.0 Universal (Public Domain Dedication).

Acknowledgements

All images are provided by the Buddhist Digital Resource Center (BDRC). This dataset was developed by Dharmaduta from specifications provided by BDRC for the project "The BDRC Etext Corpus", with funding from the Khyentse Foundation. Buddhist Digital Resource Center (BDRC). Developed by Dharmaduta / OpenPecha.