This document describes an automated license plate recognition system developed for Egypt. The system uses image processing techniques to extract license plates from images, recognizes the characters on the plates, and communicates with a database. It consists of three main parts: plate extraction, character recognition, and database communication. The system was tested on 100 plates and achieved a 91% accuracy rate. Future work could improve the system's ability to handle motion blurred or overlapped plates.

1. (ALEXANDRIA ENGINEERING
JOURNAL)
MADE BY-JASLEEN KAUR
CHANDIGARH UNIVERSITY
AUTOMATED NEW LICENSE
PLATE RECOGNITION IN EGYPT

2. Contents
 Introduction
 Methodology
 Experimental results
 Future work
 Conclusion

3. Proposed Technique
 Consists of three major parts
 Extraction of plate region
 Recognition of plate characters
 Database communication

4. Introduction
 Control of vehicles is becoming a big
problem.
 AVI systems are used for the purpose of
effective control.
 LPR is a form of AVI. It is an image
processing technology used to identify
vehicles by only their license plates

5.  Template matching methods
 Learning based methods
 Some previous algorithms have limiting
conditions like
 Limited vehicle speed
 Fixed type of license plate
 Fixed illumination

6. Methodology
 Step1: Extraction

7. Captured image of a car
 Taken from 3m by a camera

8.  There are three types of car detection in
License Plat recognition system
 Sensor detection by using infrared sensors
 Image processing methods
 Loops techniques

9. Image Processing
 This stage is divided into two sub-stages:
 Detection Stage
 Segmentation Stage

10. Detection Stage
 Captured image of a car is converted to gray
scale image

11.  Aim is to find the rectangles of plate vehicles
 Edge detection was applied by using Sobel
edge detector.
 Dilation
 Morphological algorithms
 Erosion

12.  Sobel edge detector and dilation are shown in
figure:

13.  Filling holes algorithm is used to fill the
rectangles that result from dilation process.
 Smoothing by eroding it using erosion
operation with square element to specify
candidate plate regions.
 However, there may be more than one
candidate region for plate location.

14. Filled image and Erosion Image

15. Filtering and smoothing
 Filtering and smoothing the eroded image by
using 2-D median filter with mask 5×5.
 Followed by removing unwanted objects. Fig
shows smoothing and filtering image

16. Criteria Tests
 Rectangle check
 Checking that the candidate regions for plate had
rectangle shape by compare white pixels count of
these regions to their areas with = ±5% tolerance
 If count of white pixels = ±5% area of these region
, this region may be a plate
 Else
 This region is not a plate

17.  Plate Dimension Check
 Egyptian plate had a fixed dimension with
Height= 17 cm
Width = 32 cm
So the ratio between heights to width is
approximately 1:2
 After rectangle check, the dimension check applied
on the success regions of rectangle shapes
 This region may be a plate else its not

18. Segmentation
 Objects or entities of interests are extracted
from an image for recognition processing.
 License plate is segmented into its constituent
parts obtaining the characters individually.
 The plate region is divided into three parts:
 1st part is the high part of plate region that contains
word of Egypt by Arabic and English.
 Background color of this region refers to type of
car

19.  The remainder region of plate is vertically
divided into two regions, right half contains
plate characters ad left half contains numbers.

20.  Each plate region of grey scale image and
original image was segmented into two parts
with a ratio 1:2 from height.
 Analyze the 1st part of original image using
color filter to obtain the type of car.
 Then use the 2nd part of grey scale image

21.  Filtering for enhancing the image and
removing the noises and unwanted spots.
 Remove the outer border and then inner
separator between the letters and numbers.
 Apply median filter to enhance the image

22.  Dilation operation is applied to the image for
separating the characters from each other.
 Result is image with one part containing
numbers and the other contains letters.
 This separation increases the performance of
recognition.

23.  After separation, horizontal projection is
applied to find starting and end points of
characters.
 Then the individual characters and numbers
cut from the plate.

24. Step 2: Character Recognition
 Characters and numbers are cut into blocks
with fixed size

25.  These blocks were matching with previous
database blocks of characters (27
alphanumeric characters- 17 alphabets and 10
numerical) with size of 50×25.
 Statistical correlation method was used in
matching technique
F1 (j,k) and F2 (j,k) for 1 ≤ j≤ J and 1≤ k ≤ K
represents two discrete images denoting the
image to be searched and the template
respectively

26.  Normalized cross correlation between image
pair is defined as
 The graphical user interface in MATLAB was
used to build this technique.

27. GUI character recognition

28. Step 3: Database Communication
 DB was built using Microsoft access DB.
 It depends on majority of information of a car
such that: type of car, detect a car city and
faults cost.

29.  Assemble Database- All previous information
collected in zero normal form table shown
below:

31. Networks and Servers
 UDP(User Datagram Protocol) was used to
send and receive data through wireless
network among the servers of the cities.
 By using UDP, computer applications and
datagram can send messages to other hosts on
the internet protocol (IP).

32. Experimental Results
 This technique had been experimented to
measure performance and accuracy of the
system.
 System was tested by 100 patterns under
several conditions.
 Identification was 91%
 Failed identification came from mostly motion
blurred or overlapped by other vehicles
bodies.

33. Future work and Conclusion
 Implementation works quite well however
there is still a room for improvement.
 This system was designed by GUI and UDP
under MATLAB software for recognizing new
license plate in Egypt.
 A video stream at real time was advantage of
this technique.
 Performance and accuracy were
excellent(91%)