Abstract: With an everyday increase in the number of cars on our roads and highways, we are facing numerous problems, for example: • Smuggling of cars • Invalid license plates • Identification of stolen cars • Usage of cars in terrorist attacks/illegal activities In order to address the above issues, we took up the project of developing a prototype, which can perform license plate recognition (LPR). This project, as the name signifies, deals with reading, storing and comparing the license plate numbers retrieved from snapshots of cars to ensure safety in the country and ultimately help to reduce unauthorized vehicles access and crime. License Plate Recognition (LPR) has been a practical technique in the past decades. It is one of the most important applications for Computer Vision, Patter Recognition and Image Processing in the field of Intelligent Transportation Systems (ITS). Generally, the LPR system is divided into three steps, license plate locating, license plate character segmentation and license plate recognition. This project discusses a complete license plate recognition system with special emphasis on the Localization Module.In this study, the proposed algorithm is based on extraction of plate region using morphological operations and shape detection algorithms. Segmentation of plate made use of horizontal and vertical smearing and line detection algorithms. Lastly, template matching algorithms were used for character recognition. The implementation of the project was done in the platforms of Matlab and OpenCV.

1. LICENSE PLATE RECOGNITION SYSTEM
USING
MATLAB AND OPENCV
Group Members
Asiya Zafar
Iqra Farhat
Hira Batool Rizvi
Project Supervisor
Dr. Fawad Ahmed
Department of Electrical Engineering
HITEC University Taxila Cantt.

2. BASIC MOTIVATION
With an everyday increase in the number of cars on our
roads and highways, we are facing numerous problems, for
example:

Identification of stolen cars

Smuggling of Cars

Invalid license plates

Usage of cars in terrorist attacks/illegal activities
2

3. AIM
To address these issues, we intend to develop a
prototype system in MATLAB and OpenCV which
can perform license plate recognition (LPR).
3

4. WORKFLOW

Process Digital Images of
existing/modified algorithms.
License
Plates
using

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 and
4
OpenCV is also used for localization.

5. BASIC MODULES OF THE SYSTEM

License Plate Localization
Locating the license plate in an image.

Character Segmentation
Locates the alpha numeric characters on a license
plate.

Optical Character Recognition (OCR)
Translates the segmented characters into text entries.
5

6. Block Diagram
6

7. Start
Localization
Characters And Numbers Segmentation
Feature Extraction Of Segmented
Image
Recognize The Extracted Features
Show The License Plate
End
7

8. LICENSE PLATE LOCALIZATION
Edge Detection
Morphological
Operations
Extracting The
Plate Region
8

9. Flow Chart of Localization in
Matlab
9

10. Start
Load Image From File
Convert Image Into Grayscale
Filter To Detect Edges In The Image
Morphological Operations Are Applied On The Image
Find The Connected Objects In The Image
Determine The Rectangle In The Connected Objects
Compare The Size With The Threshold Value
Determine the Coordinates Of Rectangle Using Coordinate Geometry
Retrieve The Rectangle From The Image Using The Respective Coordinates
Show The License Plate
End
10

11. LOAD THE IMAGE FROM FILE
Img=imread(‘filename’)
11

12. CROPPING AN IMAGE
img=imcrop (img,[ymins ymins Wnew Hnew])
12

13. CHANGING THE TYPE
f1=rgb2gray(img);
13

14. EDGE DETECTION
f2=edge(f1,'sobel');
14

15. MORPHOLOGICAL OPERATIONS
se=strel('rectangle',[15 17]);
se=strel(‘disk',20);
img=imfill(img,'holes');
img=imdilate(img,se);
d1-imopen(img,se);
15

16. EXTRACTING PLATE REGION
Labeling and detecting the rectangle with the set
threshold, the threshold was determined by the distance
between the car and the camera.
LP=imcrop(lp1,[xmin ymin ow oh])
16

17. CHARACTERS SEGMENTATION
Preprocessing
Horizontal And
Vertical Segmentation
17

18. PREPROCESSING
Preprocessing is very important for the good
performance of character segmentation.
Preprocessing consists of :

Determination of the image type.

Mode conversion.

Clearing objects less than a threshold value.
18

19. 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.
19

20. CHARACTERS RECOGNITION
Template Matching
Template matching is one of the most
common and easy classification method
for recognizing the characters.
20

21. TEMPLATE MATCHING
The used templates are given in the figure below:
21

22. CONTI….
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 files.
22

23. EXPERIMENTAL RESULTS
23

24. 24

25. WHY CHOSE OPENCV FOR PROJECT

To move to a Real Time Environment.

The problem of manual editing of Localization was
even fixed in OpenCV and it worked well for cars at
varying distances.
25

26. WHAT IS OPENCV

OpenCV is an open source computer vision library.

It is a collection of C functions and a few C++ classes that can
be used to implement some popular Image Processing and
Computer Vision algorithms.

OpenCV has cross-platform means It can implemented on
multiple computer platforms.

It runs on Windows and Linux. Its mainly focuses towards
real-time image processing.
26

27. Flow Chart of Localization in
OpenCV
27

28. Start
Load Image From File
Convert Image Into Grayscale
Convert Image Into Binary Image
Filter To Detect Edges In The Image
Morphological Operations Are Applied On The Image
Find The Contours In The Image
Detect The Rectangle In The Image
Retrieve The Rectangle From The Image Using The
Respective Coordinates
Show The License Plate Image
28
End

29. LOAD THE IMAGE FROM FILE
img=cvLoadImage (fileName);
29

30. CONVERT THE IMAGE INTO
GRAYSCALE IMAGE
cvCvtColor( src,dst, CV_RGB2GRAY );
30

31. CONVERT THE IMAGE INTO BINARY
IMAGE
cvThreshold(src, dst, threshold, maxValue,
CV_THRESH_BINARY);
31

32. EDGE DETECTION
cvCanny( binaryImage ,edgImage,50, 255,3)
.
32

33. MORPHOLOGICAL OPERATIONS
cvDilate( edgImage ,dImage,NULL,1);
33

34. FIND THE CONTOUR IN THE
IMAGE

cvFindContours(dImage, storage1, &contour, sizeof(CvCont
our), mode, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0));
cvDrawContours(contourImg, contour, CV_RGB(255,255,255),
CV_RGB(0, 0, 0),2,2, 8,cvPoint(0,0));

cvDrawContours(contourImg, contour, CV_RGB(255,255,25
5), CV_RGB(0, 0, 0),2,CV_FILLED, 8,cvPoint(0,0));
34

35. DETECT RECTANGLE
drawSquares( img, findSquares4( img, storage ) );
35

36. EXTRACTING PLATE REGION
36

37. RESULTS
37

38. PROBLEMS WITH THE MATLAB
SYSTEM
The problems that we faced during Localization were:

Issues of time management

Manual Changes in the code every time there is a
change in the orientation of the camera
38

39. PROBLEMS WITH THE OPENCV
SYSTEM
The problem that we faced during Localization was

On some Cars the morphological operations used in
this algorithm are insufficient to remove noise therefore
it is difficult to extract the license plate.
39

40. 40