Datasets:

BDRC
/

4-class-tibetan-script-classification-dataset

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
image_bytes: struct<bytes: binary, path: string>
  child 0, bytes: binary
  child 1, path: string
script: string
script_type: string
to
{'id': Value('string'), 'image_bytes': Image(mode=None, decode=True), 'script': ClassLabel(names=['Druma', 'Uchen', 'Danyig+Pedri', 'Gyuyig+Tsugdri'])}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              image_bytes: struct<bytes: binary, path: string>
                child 0, bytes: binary
                child 1, path: string
              script: string
              script_type: string
              to
              {'id': Value('string'), 'image_bytes': Image(mode=None, decode=True), 'script': ClassLabel(names=['Druma', 'Uchen', 'Danyig+Pedri', 'Gyuyig+Tsugdri'])}
              because column names don't match

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4-Class Tibetan Script Classification

Manuscript script classification on BDRC-style page images (4 classes).

This release uses uniform per-class sampling (600 images per class across all splits combined).

Images per class

Class	train	val	test	All
Druma	480	60	60	600
Uchen	480	60	60	600
Danyig+Pedri	480	60	60	600
Gyuyig+Tsugdri	480	60	60	600

Splits

Split	Images
train	1,920
validation	240
test	240
Total	2,400

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

Parquet schema

Column	Type	Description
`id`	string	BDRC page id (e.g. `W00KG09391-I00KG093950005`)
`image_bytes`	binary	JPEG/PNG page image
`script`	string	One of: `Druma`, `Uchen`, `Danyig+Pedri`, `Gyuyig+Tsugdri`

Shards: train/train-*.parquet, val/val-*.parquet, test/test-*.parquet.

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

Load in Python

from datasets import load_dataset

ds = load_dataset("BDRC/4-class-tibetan-script-classification-dataset")
train = ds["train"]           # 1,920
val   = ds["validation"]      # 240
test  = ds["test"]            # 240

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"])

Train a model

python scripts/upload_dataset.py --repo-id BDRC/4-class-tibetan-script-classification-dataset

Citation

@misc{bdrcscriptclass,
  title  = {4-Class Tibetan Script Classification Dataset},
  author = {Buddhist Digital Resource Center and OpenPecha},
  year   = {2026},
  url    = {https://huggingface.co/datasets/BDRC/4-class-tibetan-script-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.

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