---
license: apache-2.0
tags:
- object-detection
- yolo
- ultralytics
- manuscript
- jawi
- arabic-script
- text-line-segmentation
- document-ai
pipeline_tag: object-detection
---

# Jawi Manuscript YOLO Text Line Detector

This model is a specialized **YOLO-based Text Line Detector** fine-tuned for high-precision bounding box extraction from historical and verified **Jawi (Arabic script)** manuscripts and document folios.

## Model Highlights
- **Architecture**: Ultralytics YOLO trained with document layout inductive biases.
- **Task**: Single-class dense text line detection and bounding box localization (`line`).
- **Target Script**: Jawi (Malay written in Arabic script).
- **Post-Processing Support**: Compatible with Right-to-Left (RTL) baseline reading order sorting and Horizontal Projection Profile (HPP) line splitting.

## Usage with Ultralytics

```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download best weights from Hugging Face Hub
weights_path = hf_hub_download(
    repo_id="Shadowzer0/Jawi_YOLO_Line_Detector",
    filename="best.pt"
)

# Load model and run inference
model = YOLO(weights_path)
results = model.predict("path/to/manuscript_page.jpg", conf=0.25, iou=0.45)

for box in results[0].boxes.xyxy:
    x1, y1, x2, y2 = box.tolist()
    print(f"Detected line: ({x1}, {y1}) -> ({x2}, {y2})")
```

## Integration with Karya Agung Pipeline
This model serves as the primary line segmenter for the **Karya Agung Jawi OCR & Transliteration Studio**.