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sambett/tunisian-license-plate-recognition

Domain:

mobility

Record type:

softwaremodel
Creator:
sam
Host:
End-to-end pipeline for detecting vehicles and license plates in traffic images and reading Tunisian plate text, with an interactive Streamlit app. # πŸš— Tunisian License Plate Recognition System Complete end-to-end deep learning pipeline for automatic license plate recognition from Tunisian traffic images. --- ## πŸ“‹ Table of Contents - Overview - System Architecture - Features - Performance - Installation - Quick Start - Project Structure - Datasets - Training - Evaluation - Documentation - Results - Limitations - Future Work - Contributing - License --- ## 🎯 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 "ΨͺΩˆΩ†Ψ³") **Key Achievement:** 89.2% character-level accuracy on Tunisian license plates with Arabic text support. --- ## πŸ—οΈ System Architecture ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ INPUT IMAGE β”‚ β”‚ (Traffic Scene) β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ↓ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” 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”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ↓ [Cropped Vehicle Images] ↓ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” 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”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ↓ [Cropped Plate Images] ↓ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ …