README.md

---
license: other
license_name: fair-use-no-warranty
license_link: LICENSE
task_categories:
- object-detection
language:
- bo
tags:
- yolo
- tibetan
- document-layout-analysis
- bounding-box
size_categories:
- 1K<n<10K
pretty_name: TDLA Training Dataset v2
extra_gated_prompt: >-
  The page images in this dataset are scans of Tibetan texts from the BDRC
  digital library and are provided on a FAIR-USE basis for research. No
  copyright license is granted. By requesting access you acknowledge that you
  are solely responsible for performing your own copyright / rights analysis
  before any use, and that the Buddhist Digital Resource Center (BDRC) accepts
  no liability for any misuse of this material.
extra_gated_fields:
  Full name: text
  Affiliation: text
  Intended use: text
  I have read the copyright notice and will perform my own copyright analysis before use: checkbox
  I understand BDRC is not liable for any misuse of this material: checkbox
---

# TDLA Training Dataset v2

YOLO-format object-detection dataset for **Tibetan Document Layout Analysis (TDLA)**.
It contains bounding-box annotations for four layout classes on scanned Tibetan
document pages, split into training, validation, and test sets.

This is an expanded, re-reviewed successor to
[BDRC/TDLA-Training-Dataset](https://huggingface.co/datasets/BDRC/TDLA-Training-Dataset),
built from several annotation batches that were consolidated to a **single,
consistent annotation convention** and split to be **leakage-free**.

## Overview

| Property | Value |
| --- | --- |
| **Total annotations** | 25460 |
| **Total images** | 8325 |
| **Number of classes** | 4 |
| **Image format** | JPEG (.jpg) |
| **Label format** | YOLO (.txt) |
| **Splits** | train / val / test |
| **Split unit** | volume-level (leakage-free) |

## Image Source

All images are sourced from the [Buddhist Digital Resource Center (BDRC)](https://bdrc.io) digital library.

## Classes

| ID | Name | Annotations | % of total |
| -- | --- | --- | --- |
| 0 | header | 8155 | 32.0% |
| 1 | text-area | 10705 | 42.0% |
| 2 | footnote | 367 | 1.4% |
| 3 | footer | 6233 | 24.5% |

## Annotation Process

Annotations were created on the Ultralytics HUB platform in a two-stage workflow:

1. **Annotation** — annotators drew bounding boxes for each of the four layout
   classes (header, text-area, footnote, footer) on every page image.
2. **Quality control** — a reviewer inspected every image, verifying label
   correctness, box tightness, and class assignment. Earlier annotation batches
   were re-reviewed so that all sources follow the same convention (in
   particular, marginal header/footer elements are boxed per element,
   consistently across the whole dataset).
3. **Automated consistency audit** — a final geometric/logical audit flagged
   likely mistakes (near-duplicate or conflicting-class boxes, impossible
   header/footer/footnote orderings, out-of-bounds boxes). Flagged pages were
   manually corrected and re-imported, removing conflicting duplicate boxes.

## Split Methodology

The train / val / test split is created by grouping pages at the **volume
(book) level** and assigning each volume as a whole to a single split. This
guarantees there is **no leakage** across splits — no page (or an augmented
copy of it) and no volume appears in more than one split. The split has been
audited for pixel-identical duplicates, shared page identities, and shared
volumes across splits (all clean).

- **Footnote stratification** — the footnote class is rare, so
  footnote-bearing volumes were distributed across all three splits to keep the
  class represented everywhere.
- **Augmented data** — a subset of the training images are augmented
  (geometric/photometric) copies. These are confined to the **training set
  only**; **validation and test contain exclusively original, non-augmented
  scans**, making them a clean benchmark. Augmented images can be recognised by
  an `__aug` marker in their filename.
- Approximate ratio: ~81% train /
  ~9% val /
  ~10% test by image count.

## Split Statistics

| Split | Images |
| --- | --- |
| train | 6751 |
| val | 714 |
| test | 860 |

(train includes 1197 augmented images; val and test include 0 and 0.)

## Annotation Distribution per Split

| Class | train | val | test | Total |
| --- | --- | --- | --- | --- |
| header | 6638 | 671 | 846 | 8155 |
| text-area | 8722 | 858 | 1125 | 10705 |
| footnote | 296 | 26 | 45 | 367 |
| footer | 5046 | 540 | 647 | 6233 |

> A single image can contain multiple annotations of the same class, so
> annotation counts may exceed image counts.

## Directory Structure

```
TDLA-Training-Dataset-v2/
├── images/
│   ├── train/
│   ├── val/
│   └── test/
├── labels/
│   ├── train/
│   ├── val/
│   └── test/
├── train.txt
├── val.txt
├── test.txt
├── data.yaml
└── README.md
```

## Usage

Point your YOLO training config at `data.yaml`:

```bash
yolo detect train data=data.yaml
```

The `train.txt`, `val.txt`, and `test.txt` files list relative image paths for each split.

## Label Format

Each `.txt` label file uses standard YOLO format — one row per bounding box:

```
<class_id> <x_center> <y_center> <width> <height>
```

All coordinates are normalized to `[0, 1]` relative to image dimensions.

## Copyright & Usage Notice

This dataset does **not** come with an open-content license. The page images
are scans of Tibetan texts from the BDRC digital library and are distributed on
a **fair-use** basis for research and non-commercial layout-analysis work.

- **No copyright license is granted** over the underlying page images.
- **You are solely responsible** for performing your own copyright / rights
  analysis for your jurisdiction and intended use **before** using this
  material.
- **BDRC accepts no liability** for any misuse of this material.

By accessing the gated dataset you accept these terms.

## Acknowledgements

Developed by the [Buddhist Digital Resource Center (BDRC)](https://bdrc.io) for
the BDRC Etext Corpus. Thanks to the annotators and reviewers who produced and
consolidated the layout annotations.