Add PechaBridge line-segmentation model card

README.md

@@ -1,3 +1,129 @@
----
-license: mit
----
+---
+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.
+