---
language:
- bo
license: cc0-1.0
pretty_name: Tibetan OCR Diagnostic Benchmark (OFAT)
size_categories:
- n<1K
task_categories:
- image-to-text
tags:
- tibetan
- ocr
- synthetic
- diagnostic
- ofat
- character-error-rate
- pecha
dataset_info:
  features:
  - name: file_name
    dtype: string
  - name: transcription
    dtype: string
  - name: image
    dtype: image
  - name: script
    dtype: string
  - name: technology
    dtype: string
  - name: benchmark_group
    dtype: string
  - name: axis
    dtype: string
  - name: axis_bin
    dtype: int32
  - name: axis_value
    dtype: float64
  splits:
  - name: test
    num_examples: 300
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# Tibetan OCR Diagnostic Benchmark (OFAT)

A small, controlled **diagnostic** OCR benchmark of **300** synthetic Tibetan pecha-page images with exact, noise-free ground truth.
It is a scientific instrument for measuring how OCR **character error rate (CER)** responds to individual difficulty factors **one at a time** (OFAT) — not a coverage-maximizing training set.

**Code & regeneration:** [https://github.com/buda-base/synthetic-ocr-benchmark-tools](https://github.com/buda-base/synthetic-ocr-benchmark-tools) (`diagnostic_benchmark/`)

## What's inside

Each row is one rendered page:

| column | meaning |
|---|---|
| `image` | grayscale JPEG page (viewable in the dataset viewer) |
| `transcription` | exact LuaLaTeX-rendered ground truth (line breaks as `\\n`), Unicode-normalized for `bec-ocr-training` scoring |
| `file_name` | stable id, e.g. `axis_length_bin0_0161.jpg` |
| `script` | `uchen` |
| `technology` | `synthetic` |
| `benchmark_group` / `axis` | difficulty axis (`stack_rarity`, `repetition`, `length`, `font_size`) |
| `axis_bin` | ordinal bin (0 = easiest / center for that axis) |
| `axis_value` | numeric level (rarity score, target `rep_score`, syllables/page, or font size in pt) |

Also in the repo (not in the Arrow split): `manifest.csv` (per-image provenance) and `provenance.json` (full build config).

## Fixed center configuration

All pages share one Uchen font (**Jomolhari-id**), one page width, one image width (2000 px), no augmentation beyond minimal raster naturalization, and a coverage gate from `coverage_report/out/stack_support.parquet` so every stack is known to render faithfully.

## Axes (OFAT sweeps)

- **`stack_rarity`** — common → rare Tibetan stacks (5 bins × 16)
- **`repetition`** — char-20-gram `rep_score` from ~0 → 0.6 (5 × 16)
- **`length`** — syllables/page from 15 → 1206 (longest BEC train page + 20%; 5 × 16)
- **`font_size`** — 12 / 18 / 28 pt at fixed pixel width (3 × 20)

## Load

```python
from datasets import load_dataset

ds = load_dataset("BDRC/tibetan-ocr-diagnostic-benchmark", split="test")
print(ds[0]["file_name"], ds[0]["axis"], ds[0]["axis_bin"])
ds[0]["image"]  # PIL.Image
print(ds[0]["transcription"][:200])
```

Filter one axis:

```python
length = ds.filter(lambda r: r["axis"] == "length")
```

## Source & rights

- Text: OpenPecha BoCorpus (public domain), rendered with LuaLaTeX / HarfBuzz
- Images: synthetic, released under **CC0 1.0** (public domain dedication)
- Please cite BDRC, the BDRC Etext Corpus / OpenPecha BoCorpus, and OpenPecha Tibetan Fonts
- Builder: `https://github.com/buda-base/synthetic-ocr-benchmark-tools/tree/main/diagnostic_benchmark`

## Citation

```bibtex
@misc{bdrc_tibetan_ocr_diagnostic_benchmark,
  title  = {Tibetan OCR Diagnostic Benchmark (OFAT)},
  author = {Buddhist Digital Resource Center (BDRC)},
  year   = {2026},
  url    = {https://huggingface.co/datasets/BDRC/tibetan-ocr-diagnostic-benchmark},
  note   = {Code: https://github.com/buda-base/synthetic-ocr-benchmark-tools}
}
```