ANPR technology has surged in popularity in recent years due to its wide range of benefits for various applications. Traffic management, intelligent parking, toll automation, intelligent transportation systems in smart cities, and journey time analysis are just a few of the advantages that ANPR offers.
This project demonstrates the implementation of an Automatic Number Plate Recognition (ANPR) system using Optical Character Recognition (OCR) and computer vision techniques.
Python 3.6 or higher OpenCV Azure OCR Api Numpy Matplotlib (for visualization)
- Install the required libraries:
pip install requirements.txt
- Clone the project repository:
git clone https://github.com/MathurUtkarsh/Automatic-Number-Plate-Recognition.git
The dataset used for training and testing the ANPR system should contain images of license plates with their corresponding labels (i.e., the license plate number). The dataset can be obtained from various sources. You can use my dataset also or you can use my pretrained model to reduce yourk work.
This project shows how to use OCR and computer vision techniques to implement an ANPR system. The trained model can recognize license plate numbers in images and videos with high accuracy. The project can be further improved by using more advanced computer vision techniques and a larger dataset.