This paper describe how license plate number can be extracted in Assamese language. This was implemented in mat lab.

1. CHARACTER RECOGNITION
FROM NUMBER PLATE WRITTEN
IN ASSAMESE LANGUAGE
Presented ByPresented By
SUBHASH BASISHTHA
DEPARTMENT OF
INFORMATION TECHNOLOGY
ASSAM CENTRAL UNIVERSITY

2. Presentation Outline
 Basic Introduction to License Plate
 Types of License Plate
 Objectives Of The System
 Aim
 Basic Module Of The System
 Block Diagram of The System
 Results And Discussion
 Conclusion
 References
2

3. Introduction To License Plate
 A vehicle registration plate is a metal or plastic
plate attached to a motor vehicle or trailer for
official identification purposes[1] .
 The registration identifier is a numeric or
alphanumeric code that uniquely identifies the
vehicle within the issuing region’s database.
3

4. Plate format of License Plate
 Plates for private car and two-wheeler owners
have black lettering on a white background
[2].
e.g., (TN 81 NZ 0025).
 Commercial vehicles such as taxis and trucks
have a yellow background and black text
e.g.,
4
AP 32 VA 2223

5. Contd…
 Vehicles belonging to foreign consulates have
white lettering on a light blue background.
e.g.,
 The President of India and state governors
travel in official cars without licence plates.
Instead they have the Emblem of India in gold
embossed on a red plate[2].
5
22 UN 14

6. Current format
 The current format of the registration index consists of 3
parts, They are
 The first two letters indicate the state to which the vehicle is
registered.
 The next two digit numbers are the sequential number of a
district. Due to heavy volume of vehicle registration, the
numbers were given to the RTO(Regional Transport officers)
offices of registration as well.
 The third part is a 4 digit number unique to each plate. A
letter(s) is prefixed when the 4 digit number runs out and then
two letters and so on[3].
6
AS 32 VA 2223

7. Objectives
With an everyday increase in the number of cars on our
roads and highways, we are facing numerous problems,
for example[4]:
 Identification of stolen cars
 Smuggling of Cars
 Invalid license plates
 Usage of cars in terrorist attacks/illegal activities
7

8. Aim
To address these issues, we intend to develop a
prototype system in MATLAB which can
perform license plate recognition written in
Assamese Language.
8

9. Basic Modules of the System
 License Plate Binarization
Converting the RGB License plate image to Binary
image
 Character Segmentation
Segment the alpha numeric characters on a license
plate written in Assamese Language.
 Optical Character Recognition (OCR)
Compares the segmented characters with our
database set[5].
9

10. BlockDiagram
10

11. 11

12. Capture
 The image of the vehicle is captured using a high
resolution mobile camera and after that following
steps are performed one by one on that image[6].
12

13. Binarization
 It is the process of converting the captured image
into binary image where a fixed value is choose as
a standard threshold value and classify all pixels
with value above this threshold as a white and all
other pixels as black.
13

14. Noise Removal
 Dilation is process of improvising the quality of the
captured images. Dilation is used for filling up the
holes present in an image, can be used for joining
the broken lines and also for noise removal[7].
14

15. Segmentation
 Character segmentation can be done by using the
blob analysis.
 Matlab function “bwlabel” is used for labeling the
connected pixels together in a sequence to form
groups of connected objects.
 Function “regionprpos” is used to measure the set
of properties such as Area, Centroid, and Bounding
Box for each connected components.
15

16. Horizontal & Vertical
Segmentation
 Detect the horizontal lines in the image with a pixel
value of zero.
 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[8].
16

17. Character Recognition
 Character recognition is the process of Template
matching approach[9].
 Matching technique can be performed in three
classes.
 Direct Matching
 Deformable Template and Elastic Matching
 Relaxation Matching
17

18. Contd…
 For resizing, all the input characters images must
be equal-sized with the database characters[10].
 In our approach, we have resize each segmented
images into size 32x15.
18

19. Contd…
19
Template Matching
Template matching is one of the most
common and easy classification method for
recognizing the characters.
Template matching is one of the most
common and easy classification method for
recognizing the characters.

20. Data Set
 For recognition purpose we need a standard
database to compare the segmented characters.
 In our case we used 58 characters(out of which10
are numerals) in Assamese language[6].
20

21. Load The Image From File
21
Img=imread(‘filename’)

22. Binarization
22
f1=rgb2gray(img);

23. Morphological Operations
23
se=strel('rectangle',[15
17]);
img=imdilate(img,se);
img=imfill(img,'holes');
se=strel(‘disk',20);
d1-imopen(img,se);

24. Characters Segmentation
24

25. Normalized characters
25
After normalization

26. Data Set
The below figure shows some samples of our
dataset-
26

27. Experimental Results
27
Total License Plate Captured 100
Segmented Characters 895
Correctly Recognized Characters 845
Incorrectly Recognized Characters 50
Accuracy 94.4%

28. Conclusion & Future Work
 License Plate Recognition process requires a very high degree
of accuracy when we have to capture image from different
angle, different distance, low light etc. These types of
anomalies are needed to consider for getting better accuracy.
In this paper we have discussed License plate image taken
straightly and from 40 meter distance. So in our approach
some license image may not detect properly. In future we will
work on it to test different images from far distance and
various angles. We will also try to include more character
samples of various shape and size into our database so that to
achieve a higher level accuracy in recognition.
28

29. REFERENCES
[1] Nafiz Arica and Fatos T. Yarman-Vural. An Overview of
Character Recognition Focused on Off-Line Handwriting”
IEEE transactions on systems, man, and cybernetics—part c:
applications and reviews, vol. 31, no. 2, may 2001.
[2] M.Horowitz, “Efficient use of a picture correlator” J.Opt.
Soc. Am vol.47, pp.327,1957
[3] http://www.matlabprojecthelp.com/char-segmantation.
[4]Serkan Ozbay ,Ergun Ercelebi Automatic Vehicle
Identification by Plate Recognition World Academy Science
Engineering and technology 9 2005
29

30. Contd…
[5]Jorge Martinez-Carballido, Ruben Alfonso-Lopez, Juan M.
Ramirez-Cortes, “License Plate Digit Recognition using 7x5
Binary Templates at an outdoor Parking Lot entrance”IEEE
Trans,pp-18-21,2011.
[6]H.K.Chethan,Hemantha Kumar G. Raghavendra.R ,”A Novel
Edge Based Method to Extract Text in Camera captured
images”IEEE Trans,International Conference on Advances in
Computing, and Telecommunication Technologies,pp.853-
855,2009
[7]Serkan Ozbay, and Ergun Ercelebi, “Automatic Vehicle
Identification by Plate Recognition” proc of World Academy
of Science,Engineering and Technology,vol-9,pp.222-
225,2005
30

31. Contd…
[8]S. Banerjee, K. Mullick and U. Bhattacharya, A robust
approach to extraction of texts from camera captured images,
Proc. of the 5th International Workshop on Camera-Based
Document Analysis and Recognition (CBDAR 2013),
Washington DC, USA, pp. 53-58, 2013
[9]Prakriti Banik, Ujjwal Bhattacharya and Swapan K. Parui,
Segmentation of Bangla Words in Scene Images, Proc. of
Indian Conf. on Comp. Vision, Graphics and Image
Processing, ACM Conf. Proc. Series, 2012.
31

32. Contd…
[10]T. Chattopadhyay, U. Bhattacharya, B. B. Chaudhuri, On the
enhancement and binarization of mobile captured vehicle
identification number for an embedded solution, Proc. of
Document Analysis Systems, pp. 235-239, IEEE Comp. Soc.
Press, 2012..
32