---
language:
- bo
license: agpl-3.0
library_name: ultralytics
pipeline_tag: image-segmentation
datasets:
- openpecha/OCR-Tibetan_line_segmentation_coordinate_annotation
tags:
- tibetan
- document-ai
- line-segmentation
- instance-segmentation
- yolo11
- ultralytics
---

# PechaBridgeLineSegmentation

YOLO11n instance-segmentation model for detecting individual Tibetan text
lines on traditional pecha page scans. The model is the line-layout stage used
by [PechaBridge](https://github.com/CodexAITeam/PechaBridge) before OCR.

> **Important:** The supported PechaBridge inference pipeline applies the
> `gray` preprocessing mode (minimum RGB channel, no binarization) before YOLO
> inference. Raw Ultralytics inference can produce different results.

## Recommended usage — PechaBridge CLI

```bash
git clone https://github.com/CodexAITeam/PechaBridge.git
cd PechaBridge
pip install -r requirements.txt

# Downloads this model and the PechaBridge OCR model.
python cli.py download-models

python cli.py batch-ocr \
    --input-dir /path/to/pecha/pages \
    --ocr-model models/ocr/PechaBridgeOCR \
    --line-model models/line_segmentation/PechaBridgeLineSegmentation.pt \
    --layout-engine yolo_line \
    --line-preprocess gray \
    --ocr-engine donut
```

## Python usage

```python
from pathlib import Path

from huggingface_hub import snapshot_download
from PIL import Image
from ultralytics import YOLO

from pechabridge.ocr.line_segmentation import (
    apply_line_segmentation_preprocess,
)

model_dir = Path(snapshot_download(
    "TibetanCodexAITeam/PechaBridgeLineSegmentation"
))
weights = next(model_dir.glob("*.pt"))
model = YOLO(str(weights))

image = Image.open("page.jpg").convert("RGB")
prepared = apply_line_segmentation_preprocess(image, pipeline="gray")
results = model.predict(
    source=prepared,
    imgsz=1280,
    conf=0.25,
    verbose=False,
)

for result in results:
    print(result.boxes.xyxy)  # line bounding boxes
    print(result.masks.xy)    # line polygons
```

## Model details

- **Architecture:** Ultralytics YOLO11n segmentation
- **Task:** single-class instance segmentation
- **Class:** `line`
- **Weights:** `yolo_line_seg.pt` (about 6.0 MB)
- **Recommended input size:** `1280`
- **Recommended confidence threshold:** `0.25`
- **Recommended preprocessing:** PechaBridge `gray`
- **Ultralytics version used for the exported checkpoint:** `8.4.14`

## Training data

The training corpus was derived from
[`openpecha/OCR-Tibetan_line_segmentation_coordinate_annotation`](https://huggingface.co/datasets/openpecha/OCR-Tibetan_line_segmentation_coordinate_annotation),
converted to YOLO polygons, padded, filtered, and split locally:

| Split | Images | Label files |
|---|---:|---:|
| Train | 3,487 | 3,487 |
| Validation | 519 | 519 |
| Test | 503 | 503 |

## Validation results

Metrics stored in the released checkpoint:

| Output | Precision | Recall | mAP50 | mAP50–95 |
|---|---:|---:|---:|---:|
| Bounding boxes | 0.97736 | 0.96768 | 0.99138 | 0.61298 |
| Segmentation masks | 0.90993 | 0.90055 | 0.89697 | 0.41885 |

These numbers describe the local validation split and should not be treated as
an independent cross-collection benchmark.

## Intended use and limitations

- Intended for locating horizontal Tibetan text lines in traditional pecha scans.
- Not an OCR model; detected line crops must be passed to a text recognizer.
- Performance may degrade on modern book layouts, handwriting, vertical text,
  heavy page curvature, severe blur, unusual ornaments, or unseen collections.
- Closely spaced or touching lines may be merged; damaged lines may be fragmented.
- Predictions should be reviewed before scholarly or archival publication.

## License

AGPL-3.0. The checkpoint was trained with Ultralytics YOLO, whose open-source
software and trained model weights are distributed under AGPL-3.0 by default.
Commercial or closed-source use may require an Ultralytics Enterprise License.