Datasets:
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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
'6': multiscript
'7': non_tibetan
- name: script_type
dtype: string
splits:
- name: test
num_bytes: 0
num_examples: 480
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 |
| **multiscript** | 60 | Multi-Scripts: 60 |
| **non_tibetan** | 60 | Other: 60 |
**Total: 480 images** across 8 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`, `multiscript`, `non_tibetan` |
| `script_type` | string | Sub-script / subclass name (e.g. `DraDring`, `Multi-Scripts`, `Other`) |
## Load in Python
```python
from datasets import load_dataset
ds = load_dataset("BDRC/tibetan-script-classification-benchmark", split="test")
print(len(ds)) # 480
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**.
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