M-Essa2/Tunisian-Vehicle-License-Plate-Detection-Challenge
Record type:
project
Creator:
M-E
Host:
AI challenge to detect and recognize Tunisian vehicle license plates. Using 900 annotated car images and 900 plate text samples, participants build models for bounding box detection and OCR. Top 5 teams on Dec 7 advance to finals, with solutions applied to traffic camera monitoring
# 🚗 Tunisian Vehicle License Plate Detection & Recognition
### AI Challenge – Bounding Box Detection + OCR
> Building an end-to-end AI system to detect and recognize Tunisian vehicle license plates for intelligent traffic monitoring.
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## 🔗 Competition Link
Official Zindi Competition Page:
zindi.africa
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## 📌 Overview
This AI challenge focuses on developing a complete **Automatic License Plate Recognition (ALPR)** system for Tunisian vehicles.
Participants are provided with:
- 📸 **900 annotated car images** (bounding boxes for plates)
- 🔤 **900 plate text samples** (for OCR training)
The goal is to build:
1. 🎯 A robust license plate detection model (bounding box localization)
2. 🔎 An accurate OCR model to recognize plate characters
The **Top 5 teams (by December 7)** advance to the finals, where solutions are evaluated for real-world deployment in traffic camera monitoring systems.
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## 🎯 Objectives
- Detect Tunisian vehicle license plates from car images
- Accurately extract plate numbers using OCR
- Ensure generalization across lighting, angle, and motion conditions
- Develop models suitable for traffic surveillance systems
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## 🧠 Technical Approach
This project is divided into two main tasks:
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## 1️⃣ License Plate Detection (Object Detection)
We use object detection models to localize license plates.
### Possible Models
- YOLOv8
- Faster R-CNN
- EfficientDet
- SSD
Example (YOLO-based training):
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model.train(data="data.yaml", epochs=50, imgsz=640)
```
### Evaluation Metrics
- mAP (mean Average Precision)
- IoU (Intersection over Union)
- Precision / Recall
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## 2️⃣ Optical Character Recognition (OCR)
After detecting plates, we crop them and perform text recognition.
### OCR Approaches
- CRNN (Convolutional Recurrent Neural Network)
- Tesseract (baseline)
- Transformer-based OCR
- EasyOCR / …