README.md

---
language:
  - bo
license: mit
task_categories:
  - image-classification
tags:
  - tibetan
  - manuscript
  - script-classification
  - benchmark
  - bdrc
pretty_name: Tibetan Script Classification Benchmark
size_categories:
  - n<1K
dataset_info:
  features:
    - name: id
      dtype: string
    - name: image_bytes
      dtype: image
    - name: script
      dtype:
        class_label:
          names:
            '0': Danyig
            '1': Druma
            '2': Gyuyig
            '3': Pedri
            '4': Tsugdri
            '5': Uchen
  splits:
    - name: test
      num_bytes: 0
      num_examples: 360
  download_size: 0
  dataset_size: 0
configs:
  - config_name: default
    data_files:
      - split: test
        path: "test.parquet"
---

# Tibetan Script Classification Benchmark

Holdout benchmark for 6-class Tibetan script classification. **Test split only** — not used during training.
All images are BDRC manuscript page scans, balanced by subclass.

| Class | Images | Subclasses |
|-------|-------:|-----------|
| **Danyig** | 60 | DraDring: 25, DraRing: 9, Drathung: 17, Gongshabma: 3, Tsegdrig: 6 |
| **Druma** | 60 | Dhumri: 22, DruDring: 20, DruRing: 10, Druchen: 2, Druthung: 6 |
| **Gyuyig** | 60 | Khyuyig: 31, Tsumachug: 15, Yigchung: 14 |
| **Pedri** | 60 | Peri: 44, Petsuk: 16 |
| **Tsugdri** | 60 | Trinyig: 36, Tsugchung: 14, Tsugthung: 10 |
| **Uchen** | 60 | Uchen SugDring: 53, Uchen SugRing: 3, Uchen Sugthung: 4 |

**Total: 360 images** across 6 classes.

## 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: `Danyig`, `Druma`, `Gyuyig`, `Pedri`, `Tsugdri`, `Uchen` |

## Load in Python

```python
from datasets import load_dataset

ds = load_dataset("BDRC/tibetan-script-classification-benchmark", split="test")
print(len(ds))  # 360

row = ds[0]
# row["id"], row["image_bytes"], row["script"]
```

## Evaluate a model

```python
from experiments.benchmark_eval.eval import run_benchmark
run_benchmark(model, repo_id="BDRC/tibetan-script-classification-benchmark")
```

## Citation

```bibtex
@misc{bdrcscriptbenchmark,
  title  = {Tibetan Script Classification Benchmark},
  author = {Buddhist Digital Resource Center and OpenPecha},
  year   = {2026},
  url    = {https://huggingface.co/datasets/BDRC/tibetan-script-classification-benchmark},
  note   = {Images from BDRC. MIT.}
}
```

## Acknowledgements

Images from the **[Buddhist Digital Resource Center](https://www.bdrc.io)** (BDRC). Developed by **Dharmaduta** / **OpenPecha**.