1.     LICENSE PLATE EXTRACTION AND CHARACTER SEGMENTATION   By HINA KOCHHAR NITI GOEL Supervisor Dr. Rajeev Srivastava        

2. Introduction
License plate recognition is an image-processing technology to identify vehicles by their license plates.
All vehicles have the identity displayed (license plate) so no additional transmitter or responder is required.
These systems are country specific.

3. System Definition
The recognition of a license plate can be divided in three major parts :
Extraction of the license plate
Isolation of the individual characters
Character recognition

4. Project Focus
This project is concerned with the task of extracting the information needed to identify the characters.
The input is an image of a vehicle assumed to be speeding.
The goal is to extract the license plate and isolate the characters.

5. Indian License Plates
Licence plate number is issued by the district-level Regional Transport Office (RTO) of respective states.
All license plates are supposed to follow certain guidelines.
We assume license plates are in the correct format for our project.
SS DD AA NNNN

6. Plate Extraction
Assumptions made concerning the shape and appearance of the license plate
The license plate is a rectangular region of an easily discernable color
The width-height relationship of the license plate is known in advance
The orientation of the license plate is approximately aligned with the axes

7. Steps for Plate Extraction
Detection of white pixels using threshold method
Using a sample white image
Candidate area detection
Extracting license plate region from candidate area
Crop license plate

8. Original Image

9. Detection of white pixels using threshold value
Finding threshold using color sample
The license plates have a standard white color.
A 5 X 5 sample of this white color is taken.
The average of the matrix corresponding to this sample is found.
This is used to detect white pixels from the image.

10. Detecting white pixels in the image
For each pixel in the image, RGB values are compared with the threshold.
If they lie in the range specified by the threshold, then that pixel is assigned a value of 0.
The image obtained is subtracted from the original image to get an image in which the license plate (in RGB format) is detected.
This RGB image is then converted into grayscale image.

11. Image after white pixel detection

12. Image Containing License plate

13. Candidate Area Detection
Dilation of Image
The basic effect of the operator on the image is to gradually enlarge the boundaries of regions of foreground pixels.
Thus areas of foreground pixels grow in size while holes within those regions become smaller.

14. Finding connected components
Pixel connectivity is used to find all the connected components in the image.
Finding all connected components in an image and marking each of them with a distinctive label is called connected component labeling.

15. Finding parameters of connected components
The connected component labeled image is used to get the area and location of the components.
Minimum license plate area and height-to-width ratio have been found.
Minimum area = 2000
Minimum Height-to-Width ratio = 0.17
Maximum Height-to-Width ratio = 0.50
The connected components satisfying these values are found.
The connected component having the license plate will be at maximum depth in the image.

16. Candidate Region

17. Extracting license plate region from candidate area
The candidate area image is now used to find the license plate location by using the same steps as in candidate area detection.

18. License plate extraction using same method used in candidate region selection

19. Crop License Plate
The sum of the lines and of the columns of the image is computed, obtaining one vector for each direction.
For these two directions, first point respectively at the left and the right side of the vector that is superior or equal to the average is found, thus obtaining a rectangle to be used for cropping the plate.

20. Figure showing sum of columns in improved image

21. Figure showing sum of rows in improved image

22. Extracted License Plate

23. Conversion from gray-scale image to binary image and resizing
Gray-scale image of license plate is converted into binary image
Background of image is shown by binary value 0 and foreground are shown by binary value 1
Binary image is resized to 50 X 250

24. Binary Image of License plate

25. Resized binary image of license plate (50 X 250)

26. Character Segmentation
The extracted plate is divided into nine images, each containing one isolated character.
Since no color information is relevant, the image is converted to binary colors before any further processing.

27. Peak-to-Valley Method
A horizontal projection of a binary image of the plate reveals the exact character positions.
Changes from valleys to peaks are searched by counting the number of white pixels per column in the projection.
A change from a valley to a peak indicates the beginning of a character, and vice versa.

28. Sum of the columns graph of binary resized image of license plate

29. Images of segmented characters of license plate

30. Conclusion The results obtained are as follows : TASK SUCCESS RATE
Plate Extraction 78.4%
Character Isolation 88.9%

31. Applications
LPR applications have a wide range of applications which use the extracted plate number :
Parking
Access control
Tolling
Stolen cars
Public parking etc.

32. Future Work This project isolates the characters of the license plate.
Further work in this project will include identifying these characters.
Also the project can be generalized to identify all kinds of plates.

33. References
[1] A Licence Plate recognition tutorial,
[2] Indian License plates,
[3] Sandeep Phukan, "The scent of a scam”, The Week, January 12, 2003.
[4] M. Shridhar, J.W.V. Miller, G. Houle, L. Bijnagte, “Recognition of License Plate Images: Issues and Perspectives”, Proc. of the Fifth Intl. Conf. on Document Analysis and Recognition, pp , Sept. 1999
[5] Pixel connectivity,
[6] Connected component labeling,

34. [7]Remus Brad, “License plate recognition system”, Computer Science Department, Lucian Blaga University, Sibiu, Romania
[8]Cohen, H., Bergman, G., Erez, J., Car License Plate Recognition, Project Report, Vision and Image Sequence Laboratory, Technion, Israel.
[9] Matlab 7.0 Help