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plate_img = img.copy() #Loads the data required for detecting the license plates from cascade classifier.
![car number plate recognition python car number plate recognition python](https://img.youtube.com/vi/-WMS5JLgRQ8/0.jpg)
Let’s start simple by importing a sample image of a car with a license plate and define some functions: def extract_plate(img): # the function detects and perfors blurring on the number plate. Let’s import the libraries #importing openCV >import cv2#importing numpy >import numpy as np#importing pandas to read the CSV file containing our data >import pandas as pd#importing keras and sub-libraries >from keras.models import Sequential >from keras.layers import Dense >from keras.layers import Dropout >from keras.layers import Flatten, MaxPool2D >from import Conv2D >from import MaxPooling2D >from keras import backend as K >from keras.utils import np_utils >from sklearn.model_selection import train_test_split Step 3 We’ll start with running jupyter notebook and then importing necessary libraries in our case OpenCV, Keras and sklearn. Install some Required libraries- # installing OpenCV >pip install opencv-python=4.1.0 # Installing Keras >pip install keras # Installing Jupyter >pip install jupyter #Installing Scikit-Learn >pip install scikit-learn Step 2 Scikit-Learn: It is a free software machine learning library for the Python programming language.Keras: Easy to use and widely supported, Keras makes deep learning about as simple as deep learning can be.Haar cascade: It is a machine learning object detection algorithm used to identify objects in an image or video and based on the concept of features proposed by Paul Viola and Michael Jones in their paper “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001.Python: aka swiss army knife of coding.I have used version 4.1.0 for this project. OpenCV: OpenCV is a library of programming functions mainly aimed at real-time computer vision plus its open-source, fun to work with and my personal favorite.So, we can perform OCR (Optical Character Recognition) on it to detect the number Prerequisites:
![car number plate recognition python car number plate recognition python](https://res.cloudinary.com/practicaldev/image/fetch/s--Osv6DpG---/c_imagga_scale,f_auto,fl_progressive,h_420,q_auto,w_1000/https://thepracticaldev.s3.amazonaws.com/i/7aspr5uucso3m43epj8m.png)
Character Recognition: Now, the new image that we obtained in the previous step is sure to have some characters (Numbers/Alphabets) written on it. Again this can be done easily using OpenCV.ģ. Character Segmentation: Once we have detected the License Plate we have to crop it out and save it as a new image. This gets trickier if the image does not even have a car, in this case we will an additional step to detect the car and then the license plate.Ģ. Normally the detection algorithm is trained based on the position of camera and type of number plate used in that particular country. The accuracy can be improved if we know the exact size, color and approximate location of the number plate. We will use the contour option in OpenCV to detect for rectangular objects to find the number plate. License Plate Detection: The first step is to detect the License plate from the car.
![car number plate recognition python car number plate recognition python](https://storage.googleapis.com/kaggle-datasets-images/36674/55880/7a12abcb308171f9f5d4ecfe5679908f/data-original.png)
Steps involved in License Plate Recognitionġ.
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