Saudi License Plate OCR Model
Overview
This repository contains a custom-trained OCR model for recognizing characters from Saudi vehicle license plates as part of a complete Automatic License Plate Recognition (ALPR) pipeline.
The model processes cropped license plate images and detects individual characters, which are then decoded into the final license plate text.
Model Details
- Task: Optical Character Recognition (OCR)
- Framework: Ultralytics YOLO (PyTorch)
- Input: Cropped RGB image of a Saudi license plate
- Output: Character detections with class labels, confidence scores, and bounding boxes
Intended Use
This model is intended for:
- Automatic License Plate Recognition (ALPR)
- Smart Parking Systems
- Traffic Monitoring
- Access Control Systems
- Computer Vision Research
- Educational Purposes
Repository Contents
ocr_best_v3.pt — Fine-tuned OCR model weightsclass_names.yaml — Character class mapping used during inference
Training
The OCR model was trained on a private proprietary dataset specifically prepared for Saudi license plate character recognition.
To respect data ownership and licensing, the training dataset is not included in this repository.
Only the trained model weights are provided for inference and educational purposes.
Usage
Load the model using the Ultralytics YOLO API:
1from ultralytics import YOLO
2
3model = YOLO("ocr_best_v3.pt")
4results = model("plate_image.jpg")
The predicted character detections can then be decoded into the final license plate string using the ALPR pipeline.
Source Code
The complete ALPR system—including license plate detection, preprocessing, OCR decoding, visualization, and application logic—is available in the corresponding GitHub repository.
