Vehicle Number Plate detection using Image processing and Machine Learning techniques

Published on 04 Mar 2021. Written by Shirin Tikoo

Vehicle Number Plate detection using Image processing techniques

Vehicle Number Plate Recognition system is utilized at numerous spots like Petrol Pumps, Shopping Malls, Airports, parkways, toll corners, Hotels, Hospitals, Parking parcels, Defense and Military checkpoints, and so forth. The idea behind this the camera of this system framework catches an image of the vehicle tag and afterward, the image is prepared through multiple numbers of algorithms to give an alphanumeric transformation of the image into a text format. There are tons of ways approaches to construct this framework however here we are going to utilize python to code and machine learning to prepare the model.

SLNOTE

Project Description

The primary concepts used in the Vehicle Number Plate Recognition System are:

License Plate Detection: It is at this phase that the position of the license plate is resolved. In this phase, we get the input image of the vehicle and the output as the license plate.

Character Segmentation: At this phase, the characters on the vehicle plate are mapped out and segmented into individual images.

Character Recognition: This is the last stage of the project and the characters prior to segmented are distinguished here. and we’ll be using machine learning for this.

Python as a Programming language: Python is a widely used general-purpose, high-level programming language. It allows programming in Object-Oriented and Procedural paradigms.

Machine learning: It is one the most powerful and emerging technologies; here we are going to use machine learning to improve the accuracy of the model and for the character acknowledgment perspective i.e. map a character image to its actual character.

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Project Implementation

Capture and scan the image and then convert it to a grayscale image.
Presently, we have the grayscale image that has pixel ranges between 0 & 255. So now we have to change it to a binary image in which a pixel is either totally black or white.
Next, we have to recognize all the connected regions in the image, utilizing the CCA i.e concept of connected component analysis.
After applying CCA then we have to utilize other image pre-processing techniques like edge detection and morphological processing, noise removal, etc.
From the subsequent image, we can see that different areas that don't contain the license plate are additionally mapped. In order to eliminate these, we will utilize a few qualities of a typical license plate to remove them.
Presently it's as yet conceivable that specific regions like headlamps, stickers, and so on that look precisely like a license plate are also being marked. To take out those different areas, we'll have to do a vertical projection.
The subsequent stage is to apply the character segmentation to separate each character from the license plate. Also, we are going to use the concept of CCA here.
This will be the last stage, it's at this stage we present the idea of the concept of machine learning and all we need currently is to get a training data set, choose a supervised learning classifier, train a model, perceive how precise it is, at that point utilize the model for the prediction.
Now that we have a prepared model, we can attempt to foresee the characters that we prior segmented and here we get our vehicle number plate detection system.

Software Requirement

-Programming language - Python and Machine learning

SLDYK
Kit required to develop Vehicle Number Plate detection using Image processing and Machine Learning techniques:

Technologies you will learn by working on Vehicle Number Plate detection using Image processing and Machine Learning techniques:

Vehicle Number Plate detection using Image processing and Machine Learning techniques

Project Description

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