BDRC/Tibetan-Modern-Book-Layout-Detection

Tibetan Modern Book Layout Detection (RT-DETR-l)

An RT-DETR-l object detector that locates the four structural regions of a modern Tibetan book page — header, text-area, footnote, footer — as a preprocessing step for OCR and etext production.

• Training code, recipes & write-up: buda-base/tibetan-book-layout-analysis
• Dataset: BDRC/TDLA-Training-Dataset-v2 (gated, fair-use)

This model supersedes the earlier YOLO26m BDRC/Tibetan_Modern_Book_Layout_Detection_Model, which is retired. It is trained on a larger, re-reviewed, leakage-free dataset and evaluated on a held-out test split (not validation).

Model description

This is the tam2col variant selected from a broader model bake-off (see the blog post). It is a 4-class RT-DETR-l that was trained with the text-area boxes merged into one envelope per page, except on genuine two-column pages, where it keeps one box per column. Header and footer are kept as separate classes (they can be combined losslessly downstream).

Property	Value
Architecture	RT-DETR-l (via Ultralytics)
Task	Object detection
Image size	1024 × 1024
Number of classes	4
Framework	ultralytics (RTDETR)
Weights file	tibetan_book_layout.pt

Classes

ID	Class	Description
0	header	running title / marginal text at top or side
1	text-area	main body text (one box per column)
2	footnote	notes below the text area
3	footer	folio numbers / marginal text at bottom or side

Recommended usage — per-class confidence thresholds

The detector is deliberately recall-happy, so the best operating point differs by class. Using the Ultralytics default conf=0.25 everywhere leaves header/footer precision at only ≈0.83 and text-area at ≈0.955. Two cheap fixes, both measured with a native per-class confidence sweep on the test set:

• Raise header/footer to ≈0.60 → precision ≈0.83 → ≈0.95 (≈0.02 recall cost).
• Raise text-area to ≈0.55 → precision ≈0.955 → ≈0.98 (≈0.002 recall cost).
• Leave footnote at ≈0.25, where recall is ≈1.0 (its few false positives are high-confidence and can't be thresholded away without losing real footnotes).

class	recommended conf	P → P	R
header (0)	0.60	0.83 → 0.96	0.95
text-area (1)	0.55	0.955 → 0.98	0.995
footnote (2)	0.25	0.918	1.00
footer (3)	0.60	— → 0.94	0.95

If you need a single global threshold, 0.50 is the best compromise.

Inference

from ultralytics import RTDETR

model = RTDETR("tibetan_book_layout.pt")

# predict once at the lowest floor, then filter per class
CLASS_CONF = {0: 0.60, 1: 0.55, 2: 0.25, 3: 0.60}   # header, text-area, footnote, footer
results = model.predict("page.jpg", imgsz=1024, conf=min(CLASS_CONF.values()))

for r in results:
    for cls, conf, xywhn in zip(r.boxes.cls.tolist(),
                                r.boxes.conf.tolist(),
                                r.boxes.xywhn.tolist()):
        cls = int(cls)
        if conf < CLASS_CONF[cls]:
            continue
        print(model.names[cls], round(conf, 3), [round(v, 4) for v in xywhn])

A ready-made CLI (infer.py) with the thresholds baked in is in the GitHub repo.

Downloading the weights

from huggingface_hub import hf_hub_download
path = hf_hub_download("BDRC/Tibetan-Modern-Book-Layout-Detection",
                       "tibetan_book_layout.pt")

Performance

Evaluated on the held-out test split (860 images) of BDRC/TDLA-Training-Dataset-v2.

Native 4-class metrics

class	P	R	F1	mAP@0.5	mAP@0.5:0.95
header	0.964	0.951	0.957	0.978	0.710
text-area	0.978	0.995	0.987	0.994	0.980
footnote	0.933	0.931	0.932	0.986	0.822
footer	0.913	0.952	0.932	0.947	0.642
overall	0.947	0.957	0.952	0.976	0.788

The mAP columns are threshold-independent — they integrate over the full precision/recall curve (every confidence), so they do not depend on any operating threshold. The P / R / F1 columns are reported at each class's own maximum-F1 confidence (the standard Ultralytics val operating point), not at a fixed threshold. The per-class serving thresholds recommended above (header/footer ≈ 0.60, text-area ≈ 0.55, footnote ≈ 0.25) are the practical settings for infer.py; they land close to these max-F1 points.

Canonical 3-class metrics

Header + footer are combined into one header-footer class (matched individually), text-area is compared as one merged envelope, and footnote is left as-is — a fair space in which every layout variant was compared.

class	AP@0.5	AP@0.5:0.95	best-F1 (@conf)
header-footer	0.965	0.683	0.950 (@0.65)
text-area	0.988	0.910	0.952 (@0.89)
footnote	0.991	0.824	0.957 (@0.23)
mean	0.981	0.806

Against the other curricula in this canonical space, tam2col has the best mean AP@0.5 and the best text-area / footnote localization, and it is the only variant that handles two-column pages correctly (returning one box per column).

Training details

Parameter	Value
Base checkpoint	rtdetr-l.pt (Ultralytics)
Image size	1024
Epochs	100 (early-stopped at 60, best at epoch 40)
Patience	20
Batch size	8
GPU	single NVIDIA A10G (24 GB)

• Dataset: BDRC/TDLA-Training-Dataset-v2 — 8,325 images (6,751 train / 714 val / 860 test), volume-level leakage-free splits, augmented images confined to train.
• Label variant (tam2col): text-area boxes merged per page except on two-column pages; built with data/build_curricula.py in the GitHub repo.
• The full recipe is training/recipes/run_v5_tam2col.sh.

Intended use

Automatic layout detection of modern Tibetan book pages, as a preprocessing step for OCR pipelines, document digitization, structured text extraction, and digital-library indexing.

Limitations

• Trained on modern Tibetan books; performance on traditional pecha, manuscripts, or woodblock prints is not characterized and may be poor.
• Optimized for 1024 px input; very high-resolution scans may benefit from a higher imgsz.
• The footnote class is rare in the source material (≈1.4% of boxes); recall is strong on the test set but the class remains the least-represented.
• Header/footer boxes are small and easy to over-predict — use the recommended ≈0.60 threshold for those two classes.

License

The model weights are released under the MIT License, matching the training code. The page images used for training are not covered by any content license — they are BDRC library scans distributed on a fair-use basis. You are solely responsible for your own copyright / rights analysis before use; BDRC accepts no liability for misuse. See the dataset card for the full notice.

Acknowledgements

Developed by the Buddhist Digital Resource Center (BDRC) for the BDRC Etext Corpus, with annotations produced and consolidated on the Ultralytics platform.

Citation

@software{bdrc_tibetan_book_layout_2026,
  title   = {Tibetan Modern Book Layout Detection (RT-DETR-l)},
  author  = {Buddhist Digital Resource Center (BDRC)},
  year    = {2026},
  url     = {https://huggingface.co/BDRC/Tibetan-Modern-Book-Layout-Detection}
}