The document describes a student project to develop a car recognition system using MATLAB. The system aims to detect and recognize car number plates using image processing and optical character recognition algorithms. A group of three students divided the work, with one student writing the Matlab code, another interfacing the system with a microcontroller, and the third building the hardware. The document outlines the workflow and basic modules of the system, including license plate localization, character segmentation, and character recognition using template matching in Matlab. It also discusses some problems faced with the Matlab-based system.

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.).
2

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.
3

4. 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)
4

5. TIME DIVISION
Week 1
 Studied project
 Project Planning
Week 2
matlab coding
Week 3
 Simulation using proteus
 Hardware
Week 4
Hardware Interfacing 5

6. BASIC PROJECT
 Input image ( from real environment)
 Algorithm using (matlab)
 output
Microcontroller serial interfacing with hardware.
6

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.
8

9. Block Diagram
9

10. Start
Localization
End
Characters And Numbers Segmentation
Feature Extraction Of Segmented
Image
Recognize The Extracted Features
Show The License Plate
10

11. LICENSE PLATE LOCALIZATION
11
Edge Detection
Morphological
Operations
Extracting The
Plate Region

12. Flow Chart of extraction in
Matlab
12

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

14. LOAD THE IMAGE FROM FILE
14
a=imread(‘car 10.JPG’)

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) .

16. CHANGING THE TYPE
16
c=rgb2gray(b);

17. EXTRACTING PLATE REGION
 It is result of dilation after noise removal .
17

18. 18

19. EDGE ENHANCEMENT
19
gdiff=imsubtract(d,e);
where ‘d’ is dilated and
‘e’ is eroded image

20. MORPHOLOGICAL OPERATIONS
 Filling (holes )
 Thinning ( for character isolation)
 Finding connected components of area more than
200 pixels
20

21. CHARACTERS SEGMENTATION
21
Preprocessing Horizontal And
Vertical Segmentation

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.
22

23. CHARACTERS RECOGNITION
23
Template Matching
Template matching is one of the most
common and easy classification method
for recognizing the characters.
We used code for OCR

24. TEMPLATE MATCHING
The used templates are given in the figure below:
24

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.

26. 26

27. 27

28. EXPERIMENTAL RESULTS
28

29. WHY CHOSE MATLAB FOR PROJECT
 To move to a Real Time Environment.
 For fast computation.
29

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.
30