This document summarizes a seminar on MATLAB software presented by representatives from CETPA Institute. It discusses the history and development of MATLAB, file types used in MATLAB, basic functions and operations in MATLAB, types of projects that can be done in MATLAB including image processing and signal processing. It provides details of a sample license plate detection project implemented in MATLAB, outlining the basic modules, flow chart, and experimental results. MATLAB was chosen for this project due to its capabilities for fast computation and transitioning to a real-time environment. Some problems faced included the algorithm not working perfectly on multiple images and requiring manual code changes each time.

1. SEMINAR ON
SUMMER TRAINING ON MATLAB
SOFTWARE BY CETPA INSTITUTE
REPRESENTED BY MOHD AHAD 201310104110011
AKSHAY PATEL 201410104110701
DEPARTMENT OF ELECTRONICS AND COMMUNICATION

2. Innovation of MATLAB
● MATLAB was developed by Math works, which was originally
created to provide easy access to tire data matrix line pack dan
Eisack projects.
● Short for Matrix laboratory MATLAB was invented in the late
1970s by Cleve Molar.
● He was the chairman of the computer Science department of the
university of new matrix.
● Now a days number of alternative of MATLAB are available for
example science lab, Mathematica, Maple, nag Dsl.

3. TYPE OF FILES
.m FILES-Script Files and Function Files(M-FILES) .mat files-Binary Data Files(MAT FILES)
.fig file-figure file
.mld file-simulink

4. + ADDITION
- SUBTRACTION
● MULTIPLICATI
ON DIVISION
^ EXPONENTIATION
(to the power of)
&& “AND”
|| “OR”
~ “NOT”
< “LESS THAN”
> “GREATER THAN”
<= “LESS THAN OR
EQUAL TO”
>= “GREATER THAN OR
EQUAL TO”
== “IS EQUAL TO”
~= “IS NOT EQUAL TO”
OVERVIEW: MOST OPERATORS
1.ARITHMETIC 2.RELATIONA
L
3.BOOLEAN

8. Basic functions
Mathematics- Arrays and matrices, linear algebra, other areas of mathematics
Data Analysis- Basic data operations, descriptive statistics,covariance and correlation, filtering
and convolution, numerical derivatives and integrals, Fourier transforms, time series analysis
Programming
Data Types- Function/expression evaluation, program control, function handles, object
oriented programming, error handling, operators, data types, dates and times, timers
File images
Graphics -Line plots, annotating graphs, specialized plots, images, printing,
3-D Visualization-Surface and mesh plots, viewcontrol , lighting and transparency, volume
visualization
Creating Graphical User Interfaces GUIDE- programming graphical user interfaces
External Interface-Interfaces to DLLs, Java, COM and ActiveX, Web services, and serial port
devices, and C and Fortran routines

9. TYPE OF PROJECT ON MATLAB
+ IMAGE PROCESSING
+ SIGNAL PROCESSING
+ POWER ELECTRONIC
+ COMMUNICATION

10. PROJECT:NUMBER PLATE DETECTION SYSTEM

11. PROJECT:NUMBER PLATE DETECTION SYSTEM
Input:vechile image
RGB TO GRAY SCALE CONVERSION
MORPHOLOGICAL OPERATON
EXTRACTION ACCURATE LOACTION OF THE LICENCE PLATE
IMAGE ENHANCEMENT
CHARATER SEGMENTATION
OUTPUT:CHARATER RECOGNISTION

12. Why did we choose this project?
● Identification of stolen cars
● Smuggling of Cars
● Invalid license plates
● Usage of cars in terrorist attacks/illegal activities
● Applications in traffic systems (highway electronic toll collection,
red light violation enforcement, border and customs checkpoints,
etc.).

13. Basic Modules of the System
● Detection is done by Character Segmentation
Locates the alpha numeric characters on a license plate.

14. Basic Modules of the System
● Optical Character Recognition (OCR)
Translates the segmented characters into text entries

15. BLOCK DIAGRAM

16. Flow Chart of extraction in
Matlab

18. Load The Image From File
a=imread(‘car 10.JPG’)

19. Preprocessing
Preprocessing is very important for the good performance of
character segmentation.
Preprocessing consists of :
● Resizing image
● Rgb to gray
● Noise removal ( we used median filter) .

20. Changing the TYPE
c=rgb2gray(b);

21. Extracting Plate Region
● It is result of dilation after noise removal .

22. Edge enhancement
gdiff=imsubtract(d,e);
where ‘d’ is dilated and
‘e’ is eroded image

23. Morphological Operations
● Filling (holes )
● Thinning ( for character isolation)
● Finding connected components of area more than 200 pixels

24. Characters Segmentation

25. Horizontal & Vertical Segmentation
● Detect the horizontal lines in the image with a pixel value of
zero.
● Converting the image into binary.
● 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.

26. Characters Recognition

27. Template Matching
The used templates are given in the figure below:

28. Experimental Results

29. SOURCE IMAGE
BINARIZED IMAGE

30. Why choose Matlab for project ?
● To move to a Real Time Environment.
● For fast computation.

31. Problems with the MATLAB System
The problems that we faced during Localization were:
● Algorithm did not work perfectly for more than one image.
● Manual Changes were required in the code every time , manually
we had to change parameters in code that was kind of hit and trial
method.

32. THANK YOU