Datasets:
File size: 2,085 Bytes
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license: mit
task_categories:
- image-classification
tags:
- tibetan
- manuscript
- script-classification
- benchmark
- bdrc
pretty_name: Script Classification Benchmark
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': Druma
'2': Gyuyig
'3': Pedri
'4': Tsugdri
'5': Uchen
- name: source
dtype: string
- name: font_name
dtype: string
splits:
- name: benchmark
num_bytes: 0
num_examples: 540
download_size: 0
dataset_size: 0
configs:
- config_name: default
data_files:
- split: benchmark
path: "benchmark.parquet"
---
# Script Classification Benchmark
Holdout benchmark for six-class Tibetan script classification (540 page images).
Each class combines BDRC manuscript scans and synthetic benchmark images from
``Data/benchmark/``.
| Class | Images |
|-------|-------:|
| Danyig | 90 |
| Druma | 90 |
| Gyuyig | 90 |
| Pedri | 90 |
| Tsugdri | 90 |
| Uchen | 90 |
## Parquet schema
| Column | Type | Description |
|--------|------|-------------|
| `id` | string | BDRC page id or synthetic sample id |
| `image_bytes` | binary | JPEG/PNG page image |
| `script` | string | One of the six script families |
| `source` | string | ``bdrc`` or ``synthetic`` |
| `font_name` | string | Synthetic font name (version suffix stripped); empty for BDRC scans |
## Load in Python
```python
from datasets import load_dataset
repo = "BDRC/script-classification-Benchmark"
ds = load_dataset(repo, split="benchmark")
print(len(ds), ds.column_names) # 540, ['id', 'image_bytes', 'script', 'source', 'font_name']
row = ds[0]
img = row["image_bytes"] # bytes — same schema as BDRC training Parquet
print(row["source"], row["font_name"])
```
Page-level BDRC holdout ids for training exclusion live in
``benchmark_page_ids.json`` in the local ``Data/benchmark/`` folder.
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