BDRC/Tibetan-Modern-Book-Layout-Detection
Object Detection • Updated
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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.
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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, 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) 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:
- Annotation — annotators drew bounding boxes for each of the four layout classes (header, text-area, footnote, footer) on every page image.
- 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).
- 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
__augmarker 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:
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) for the BDRC Etext Corpus. Thanks to the annotators and reviewers who produced and consolidated the layout annotations.
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