CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
Number plate recognition system using matlab.
1. 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
2. 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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3. 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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6. BASIC PROJECT
Input image ( from real environment)
Algorithm using (matlab)
output
Microcontroller serial interfacing with hardware.
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7. 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
8. 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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13. 13
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
15. 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) .
22. 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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25. 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.
29. WHY CHOSE MATLAB FOR PROJECT
To move to a Real Time Environment.
For fast computation.
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30. 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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Notas del editor
Localization is basically highlighting some parts and ignoring other stuff in background .
Egde (enhancement + brightening by (diff + convolve)
Morphological ( e, d , fill , thin )
connected components
Then edge brightening was done by convolve command