| ---
|
| license: apache-2.0
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| tags:
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| - object-detection
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| - yolo
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| - ultralytics
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| - manuscript
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| - jawi
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| - arabic-script
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| - text-line-segmentation
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| - document-ai
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| pipeline_tag: object-detection
|
| ---
|
|
|
| # Jawi Manuscript YOLO Text Line Detector
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|
|
| 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.
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|
|
| ## Model Highlights
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| - **Architecture**: Ultralytics YOLO trained with document layout inductive biases.
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| - **Task**: Single-class dense text line detection and bounding box localization (`line`).
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| - **Target Script**: Jawi (Malay written in Arabic script).
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| - **Post-Processing Support**: Compatible with Right-to-Left (RTL) baseline reading order sorting and Horizontal Projection Profile (HPP) line splitting.
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|
|
| ## Usage with Ultralytics
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|
|
| ```python
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| from ultralytics import YOLO
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| from huggingface_hub import hf_hub_download
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|
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| # Download best weights from Hugging Face Hub
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| weights_path = hf_hub_download(
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| repo_id="Shadowzer0/Jawi_YOLO_Line_Detector",
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| filename="best.pt"
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| )
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|
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| # Load model and run inference
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| model = YOLO(weights_path)
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| results = model.predict("path/to/manuscript_page.jpg", conf=0.25, iou=0.45)
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|
|
| for box in results[0].boxes.xyxy:
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| x1, y1, x2, y2 = box.tolist()
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| print(f"Detected line: ({x1}, {y1}) -> ({x2}, {y2})")
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| ```
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|
|
| ## Integration with Karya Agung Pipeline
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| This model serves as the primary line segmenter for the **Karya Agung Jawi OCR & Transliteration Studio**.
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|
|
