This document summarizes a student project to develop number plate recognition using MATLAB. The objectives are to identify the number plate region on a vehicle, and recognize the numbers and letters. The methodology involves capturing an image, preprocessing it, extracting the plate region, segmenting individual characters, and recognizing each character by comparing it to templates. Some key assumptions are that the vehicle position is fixed, there is single lane traffic, and illumination is provided. Potential applications include vehicle security, parking management, toll collection, and law enforcement.

1. Number plate Recognition using MATLAB
Group Members-
1. Abhishek Sainkar
2. Ashish Kumar
3. Prajwal Durugkar
4.Munna Kumar

2. OBJECTIVE OF THE PROJECT
• Identifying the number plate Region on
the vehicle.
• Identifying the numbers and alphabets
on the number plate.

3. ASSUMPTIONS :
• The position of the car is fixed as it is
placed in a marked box.
• Single Entry & Exit Gate System.
• Single Lane Traffic.
• Artificial Illumination is provided at all
times.

4. NUMBER PLATE RULES :
A. Positioning
• Height should not exceed 1 metre
from the ground level.
• The size of the number plate plate
is 340x 200 mm.
• The letters of the registration mark
shall be in English and figures in
Aerobic numerals.
The MV Act (Rule 50, 51 of MV Act, 1989)
specifies that the

5. METHODOLOGY
1. Capture Image
2. Pre Processing
3. Plate Region Extraction
4. Character Identification
5. Character Segmentation

6. WORKING:

7. Step 1: Image Acquisition
• In this step image is captured from
digital camera. Image should be taken
from fixed angle parallel to horizon.

8. Step 2: Convert into Gray image
• This algorithm works on Gray level image, for
pre- processing and identifying the required
information.
• In this step coloured image is converted into
the Gray scale image. Gray scale image is
shown in figure 3.

9. Step 3: Dilation of an Image
• In this step, image has been dilated. Dilation
is a process for filling holes in an image,
sharpen edges of an object maximize
brightness and connect the broken lines.
• Dilation can remove unwanted noise from
image. Dilated image is shown in figure 4.

10. Step 4: Convert into Binary Image
• Image is converted into binary image from Gray
scale.
• Intensity change value is calculated easily as
compared to Gray scale and colour image.
Binary image is shown in figure 9.

11. Step 5: Segmentation of alphanumeric character
• Individual alphanumeric characters are
segmented. Segmentation has been done by
using smearing algorithms in both horizontal
and vertical histogram.
• Each individual alphanumeric character is
extracted by finding starting and ending points
of character in horizontal direction. These
characters are shown in figure 10.

12. Step 6: Recognition of individual character
• For Recognition of individual alphanumeric
character, template based Recognition method
is used.
• In template based algorithm, segmented image
is compared with one image which is stored in
database named as template image.
• In both images best matched similarity is
compared. This similarity is matched with
statistical method correlation. These template
images are shown in figure 11.

13. APPLICATION OF THE PROJECT
• Society Security – It can act as complete
solution for vehicle tracking
• Vehicle parking – Vehicle location guidance,
partially or fully automated payment process.
• Motorway road tolling – reduces required
manpower to process payments.
• Law enforcement – identifying stolen vehicles
based on the blacklist.

14. Thank You