number plate recognition system using matlab

CAR RECOGNITION SYSTEM USING

MATLAB

Project Supervisor

Sir Umer Javed

Group Members

Sania Arif (1547)Namra Afzal (1528)

Laraib Mumtaz (1522)

Batch F11 BSEE Faculty of Engineering and

Technology IIUI

WHY DID WE CHOOSE THIS PROJECT?

Identification of stolen cars

Smuggling of Cars

Invalid license plates

Usage of cars in terrorist attacks/illegal activities

Applications in traffic systems (highway electronic toll collection, red light violation enforcement, border and customs checkpoints, etc.).

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AIM

We intended to develop a system in MATLAB which can perform detection as well as recognition of Car Number plate

  The objective of this project is to recognize car number plate using serial communication.

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WORK DIVISION

Matlab code ( Namra Afzal 1528 /BSEE /FET F11)

Microcontroller interfacing (Sania Arif / 1547 /BsEE FET f11)

Hardware ( Laraib Mumtaz/ 1522/ BSEE /FET F11)

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TIME DIVISION Week 1 Studied project Project PlanningWeek 2 matlab coding Week 3 Simulation using proteus Hardware Week 4 Hardware Interfacing

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BLOCK DIAGRAM:INPUT IMAGE     

ALGORITHM USING (MATLAB)   OUTPUTMICROCONTROLLER

BASIC PROJECT

Input image ( from real environment)

Algorithm using (matlab)   outputMicrocontroller serial interfacing with hardware.

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WORKFLOW

Image was taken from real environment . Process Digital Images of License Plates using

existing/modified algorithms.

Algorithms will perform alpha numeric conversions on the captured license plate images into text entries.

System would check the extracted entries against a database in real time.

The entire system is implemented in MATLAB is used for detection and recognition . 7

BASIC MODULES OF THE SYSTEM

Detection is done by Character Segmentation Locates the alpha numeric characters on a license plate.

Optical Character Recognition (OCR)Translates the segmented characters into text entries.

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Block Diagram

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Start

Localization

End

Characters And Numbers Segmentation

Feature Extraction Of Segmented Image

Recognize The Extracted Features

Show The License Plate

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LICENSE PLATE LOCALIZATION

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Edge Detection

Morphological

Operations

Extracting The Plate

Region

Flow Chart of extraction in Matlab

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Start

Load Image From File

Morphological Operations Are Applied On The Image

Convert Image Into Grayscale

Median Filter To remove noise in The Image

Edge enhancement In The Image

Convolution for brightening image

Intensity scaling

Show The License Plate

Filling all the regions of Image

Thinning to isolate characters

End

LOAD THE IMAGE FROM FILE

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a=imread(‘car 10.JPG’)

PREPROCESSING Preprocessing is very important for the

good performance of character segmentation.

Preprocessing consists of :

Resizing image

Rgb to gray

Noise removal ( we used median filter) .

CHANGING THE TYPE

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c=rgb2gray(b);

EXTRACTING PLATE REGION

It is result of dilation after noise removal .

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EDGE ENHANCEMENT

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gdiff=imsubtract(d,e);

where ‘d’ is dilated and ‘e’ is eroded image

MORPHOLOGICAL OPERATIONS

Filling (holes ) Thinning ( for character isolation) Finding connected components of area more

than 200 pixels

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CHARACTERS SEGMENTATION

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PreprocessingHorizontal And

Vertical Segmentation

HORIZONTAL & VERTICAL

SEGMENTATION

Detect the horizontal lines in the image with a pixel value of zero.

Converting the image into binary.

Use simple “for loops” to detect the portions of the image that had connected objects with a pixel value of ‘0’ and hence accordingly, the image was read.

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CHARACTERS RECOGNITION

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Template Matching

Template matching is one of the most common and easy

classification method for recognizing the characters.

We used code for OCR

TEMPLATE MATCHINGThe used templates are given in the figure below:

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OUTPUT

Correlation is used to match the image from the

license plate and the template’s image. The

following figure shows the numbers in a text file.

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EXPERIMENTAL RESULTS

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WHY CHOSE MATLAB FOR PROJECT

To move to a Real Time Environment.

For fast computation.

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PROBLEMS WITH THE MATLAB SYSTEM 

The problems that we faced during Localization were:

Algorithm did not work perfectly for more than one image.

Manual Changes were required in the code every

time , manually we had to change parameters in

code that was kind of hit and trial method.

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