Datasets:

BDRC
/

script-classification-Benchmark

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

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.