1. License Plate Recognition of A Vehicle using MATLAB
Under the guidance of
Prudhvi Raj By
Chetana
{Y07EI021}
C.Madhuri
{Y07EI019}
Vinod
{Y07EI045}
M.Praveen Kumar
{Y07EI055}

2. Contents: Aim Introduction Other Names Elements of LPR Algorithms
Operation

3. Aim:
Recognition of license number of a vehicle by the detection of its license plate from the vehicle image captured by the camera using MATLAB programming.

4. Introduction:
What is LPR……………….?
LPR

5. License Plate Recognition {LPR}
LPR {License Plate Recognition} is an image-processing technology used to identify vehicles by their license plates.
This technology is used in various security and traffic applications.

6. Also Called As: Automatic Vehicle Identification (AVI)
Car Plate Recognition (CPR)
Automatic Number Plate Recognition (ANPR)
Car Plate Reader (CPR)
Optical Character Recognition (OCR) for Cars

7. Elements of LPR
License-Plate Recognition System consists of three main modules:
Extraction of plate region
Character segmentation
Optical Character Recognition

8. Algorithms: Extraction of plate region Smearing Algorithm
Edge Detection Algorithm
Character segmentation
Filtering Algorithm
Morphological Algorithm
Optical Character Recognition Statistical Algorithm
Template Matching

9. Operation:
Image captured from the camera is first converted to the binary image {only black and white} consisting of only 1’s and 0’s by thresholding the pixel values of 0 (black) for all pixels in the input image with luminance less than threshold value and 1 (white) for all other pixels.

10. Extraction of Plate Region
To find the plate region, firstly smearing algorithm is used.
Smearing Algorithm: It is a method of extraction of text areas on a mixed image. The image is processed along vertical and horizontal runs. If the number of white pixels is less than a desired threshold or greater than any other desired threshold, white pixels are converted to black.

11. The image is converted into binary coding & smearing is applied.
Fig{1}: Original Image
The image is converted into binary coding & smearing is applied.
Fig{2}: After smearing algorithm
Fig{1}
Fig{2}

12. After smearing, a morphological operation, dilation, is
applied to the image for specifying the plate location.
However, there may be more than one candidate region for
plate location. To find the exact region and eliminate the other
regions, some criteria tests are applied to the image by
smearing and filtering operation. The processed image after
this stage is as shown in Figure 2(a) and image involving only
plate is shown in Figure 2(b).

14. After obtaining plate location, region involving only plate is cut giving the plate as shown in Figure 3.

15. Elements of typical LPR systems
Cameras
Illumination
Frame grabber
Computer
Software
Hardware
Database

16. Image Acquisition
License Plate Extraction
Segmentation
Recognition

17. Commercial Products IMPS (Integrated Multi-Pass System) Perceptics
Vehicle Identification System for Parking Areas (VISPA)
Hi-Tech Solution

18. Applications of LPR Systems
Law Enforcement
Parking
Automatic Toll Gates
Border Crossing
Homeland Security

19. Example of one application:

20. Techniques:
automatic number-plate recognition using optical character recognition techniques
knowledge-guieded boundary following and template matching for automatic vehicle identification.
bidirectional associative memories (BAM) neural network for number plate reading.
vertical edge using Hough transform (HT) for extracting the license plate
neural network for color extraction and a template matching to recognize characters.
genetic algorithm based segmentation to extract the plate region