BDRC/8-class-tibetan-page-classification-dataset

8-Class Tibetan Page Classification

Page-level classification of BDRC manuscript / print images into 8 classes: the six script categories of BDRC/6-class-tibetan-script-classification-dataset plus two page-type classes — blank and nonplaintext — so a downstream OCR pipeline can route pages (skip blanks, handle tables/illustrations/scores separately).

Trained classifier: BDRC/8-class-tibetan-page-classifier.

Classes

Class	Description
danyig_pedri	Danyig / Pedri script family
druma	Druma (Drutsa) script family
gyuyig_tsugdri	Gyuyig / Tsugdri script family
multiscript	pages containing several scripts
non_tibetan	non-Tibetan script
uchen	Uchen (block/print) script
blank	blank / mostly-blank / bleed-through pages (no readable text)
nonplaintext	non-plain-text pages: tables / structured layouts, illustration-only pages, musical scores

Images per class

Class	train	val	test	All
danyig_pedri	480	60	60	600
druma	480	60	60	600
gyuyig_tsugdri	480	60	60	600
multiscript	480	60	60	600
non_tibetan	480	60	60	600
uchen	480	60	60	600
blank	480	60	60	600
nonplaintext	279	63	59	401

The six script classes reuse the balanced 6-class data (480 / 60 / 60 per class). blank adds 600 reviewed blank / bleed-through pages (480 / 60 / 60). nonplaintext is a smaller, hand-curated open-access set.

Splits

Work-stratified split — every manuscript work (W-id) appears in exactly one of train / val / test (no leakage across splits).

Split	Images	Works
train	3,639	1881
validation	483	242
test	479	413
Total	4,601

Page-level split manifest: splits/8way_combined.json.

Parquet schema

Column	Type	Description
id	string	page id
image_bytes	image	JPEG page image
script	class_label	one of: danyig_pedri, druma, gyuyig_tsugdri, multiscript, non_tibetan, uchen, blank, nonplaintext

Shards: train/train-*.parquet, val/val-*.parquet, test/test-*.parquet. See split_stats.json / split_stats.md.

Load in Python

from datasets import load_dataset

ds = load_dataset("BDRC/8-class-tibetan-page-classification-dataset")
train, val, test = ds["train"], ds["validation"], ds["test"]
row = train[0]
print(row["id"], row["script"])   # script is a ClassLabel int; ds.features["script"].int2str(...)
img = row["image_bytes"]          # PIL image

Citation

@misc{bdrc8classpageclass,
  title  = {8-Class Tibetan Page Classification Dataset},
  author = {Buddhist Digital Resource Center and OpenPecha},
  year   = {2026},
  url    = {https://huggingface.co/datasets/BDRC/8-class-tibetan-page-classification-dataset},
  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.