End-to-end pipeline for detecting vehicles and license plates in traffic images and reading Tunisian plate text, with an interactive Streamlit app.
# π Tunisian License Plate Recognition System
Complete end-to-end deep learning pipeline for automatic license plate recognition from Tunisian traffic images.
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## π Table of Contents
- Overview
- System Architecture
- Features
- Performance
- Installation
- Quick Start
- Project Structure
- Datasets
- Training
- Evaluation
- Documentation
- Results
- Limitations
- Future Work
- Contributing
- License
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## π― Overview
This project implements a **three-stage deep learning pipeline** for Tunisian license plate recognition:
1. **Vehicle Detection** - Detect and crop vehicles from traffic scenes
2. **Plate Detection** - Locate license plates within vehicle images
3. **OCR Recognition** - Extract text from detected plates (supports Arabic "ΨͺΩΩΨ³")
**Key Achievement:** 89.2% character-level accuracy on Tunisian license plates with Arabic text support.
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## ποΈ System Architecture
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β INPUT IMAGE β
β (Traffic Scene) β
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β
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β STAGE 1: Vehicle Detection β
β β’ Model: YOLOv8n (3M params) β
β β’ mAP@50: 76.6% β
β β’ Speed: ~10ms β
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β
[Cropped Vehicle Images]
β
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β STAGE 2: Plate Detection β
β β’ Model: YOLOv8n (3M params) β
β β’ mAP@50: 98.96% β
β β’ Speed: ~15ms β
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β
[Cropped Plate Images]
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