CHAPTER 1

INTRODUCTION

In general, vehicle’s license plate number is a very unique combination of

letters and numbers which can be describe as the identity card for the respective

vehicle. The existence of the plate to represent each vehicle can be used in tracking

system where the vehicle is identified and the owner is tracked down through the

database. To do this, the recognition process to read the characters on license plate is

critical. This can be done by human process, where our brain interprets the image

transfer from our eyes automatically. However, there is also another way to do

collect vehicle data without human intervention by designing a computerized plate

recognition system.

Plate recognition system has been a very popular research topic worldwide,

where various systems had been developed corresponding to the specifications of

license plate system for each country. This is because vehicle identification can be

implemented in various applications that can help in controlling the access to secured

area, entrance admission, security enforcement, non-stop tolling system, ticket-free

parking lot, speed-limit enforcement, and road traffic control. Different approaches

and techniques had been used to develop this system to achieve the best performance

on the recognition percentages.

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The purpose of this project is to implement and utilize the Real-Time Special

Plate Recognition (R-TSPR) system developed by Samsul bin Setumin for his master

thesis in UTM during year 2009 using C++ language to increase the performance of

system towards real time implementation. The system will be implemented into a

vehicle flow monitoring system which detect and record the inflow and outflow of

vehicles at the entrance.

1.1 Problem Statement

In implementing the controlling and monitoring of the vehicles access to a

restricted place, such as residential area, the current tracking system depend mostly

on manual process. For example, control of the vehicle flow by issuing of car

registration sticker to the registered vehicles. Monitoring of such control system can

only be done manually by recording the data into the database by the security guard.

The recording process is troublesome where the process of searching the respective

vehicle’s database and recording manually time involved is needed. The process is

not practical especially during the peak hours. On the other hand, by using the touch

and go gate system, the entering or leaving of a registered vehicle can be recorded

automatically into the database by the system automatically. However, when come to

unregistered car, the recording process have to be done manually. To monitor the

vehicle flow in a more effective and efficient way, an integrated real time system

shall be introduced to recognize the car plate automatically and at the same time,

recording the vehicle flow data into the existing database automatically or create a

new database for the under unregistered vehicle for the first time enter.

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1.2 Objective

The main objective of this project is to develop an integrated real time system

that can detect the incoming and outgoing vehicle flow of an area. The system should

also monitor and keep the record for the flow of each vehicle into the system

database. To achieve this objective, the system created should be able to capture the

vehicle image from the CCTV video, detect, locate and recognize the car plate

number of each vehicle, match and record the inflow and outflow activity of the

vehicle into the available database or create a new database if the record not found,

and attach a time stamp to each data to indicate the time of the vehicle in or out of

the entrance.

1.3 Scope

The system is designed mainly to recognize the different types of car plate

including car, truck, bus, van and so on. However, due to the limitation to the

position of video recorder to capture the sample right in front of the vehicle

(perpendicular to the camera view), the detection motorcycle car plate shall not be

included for the implementation of the system. In addition, the system is

implemented with the assumption of the minimum noise input, in which the sample

shall be a high quality video input taken during day time, which the situation of

heavy rainy day and night time are not taken into consideration. The implementation

of the sample video processing will be done based on the grayscale binary images

using stroke analysis technique to recognize the car plate. The system is implemented

on Microsoft Visio software using C++ programming language. The database used in

the system will be created using MySQL software. MySQL/C++ connector will be

used for the communication of the system created with the database.

CHAPTER 2

LITERATURE REVIEW

Vehicle license plate recognition system as an automatic vehicle

identification system has been a popular research topic in image processing field for

years. The algorithms involve 4 major processes, which are image pre-processing,

license plate localization, plate characters extraction, and character recognition

process. Due to the difference in the various specifications to define the license plate

in each country and the different language used, the application of the system

developed is restricted to the country used. According to researchers, different

algorithms and techniques had been designed for different license plate specifications

and to achieve better performance of the recognition process.

2.1 Plate Localization Technique

License plate location is the process used to locate the position of the car

plate in the image. Instead of perform image processing throughout the whole image,

the position of the license plate is first determined and extracted. The recognition

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process is then performed only on the extracted license plate area to reduce

calculation involved. The proposal of this technique had made the implementation of

the license plate recognition system in real time basis possible where the

performance of the localization of plate position influence most to the execution time

for whole system. If the license plate is successfully located, the successful rate for

the recognition process should increase.

In year 1998, Hans A.Hegt, Ron J. De la Haye, and Nadeem A Khan had

develop an High Performance License Plate Recognition System that use corner’s

point extraction technique where the detector will detect 4 possible corners of license

plate and check the spatial frequency of the plate area. In this algorithm, template

matching technique is used to detect the corner of the plate. When there are four

corner points detected with quadrangle shape, the spatial frequencies of the contents

inside the area is checked. If the spatial frequencies detected is as expected (spatial

frequencies contribute by characters), the area is verified as the license plate position.

Later in year 2000, Mei Yu, Yong Deak Kim had proposed a Korean vehicle

license plate recognition system based on vertical edge matching technique to locate

the position of license plate. The algorithm is based on the observations that most of

the vehicles consist of more horizontal lines as compared to vertical lines. Thus, the

two vertical edges of the license plate are selected to verify the four corners of the

license plate. Next, a binary size-and-shape filter is used as region identification to

label the region of interest. The edge area of vertical edge image is defined as a white

pixel sel with eight connected neighbour pixels with each other. The filtering process

will filter out the edge candidate that does not conform to predefined features as

below:

Edge area size

Form beeline with predefined interval slope

Similar slope where top and bottom is close to one another

Ratio of width to height is about 2:1, where the possible range is between

1.4 : 1 < r < 3.3 : 1

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Two vertical edges with similar height, where the ratio of the both edge is

between 0.8 < r < 1.2

After all possible edge of the license plate area is detected, the area is

examined with the percentage of the characters region in the area, which is refers to

the white pixels in between the region. The existence of the percentage in between

10% to 40% of white pixels in the region is considered as the real license plate

region.

After 3 years, V. Koval, V. Turchenko, V. Kochan, A. Sachenco, G Markowsky had

used iterative thresholding operation to determine the license plate region in their

license plate recognition system. Using this algorithm, the object (white pixels) exist

in the image is first identified, labeled, and then analyzed. The analysis is based on

the geometrical characteristics of license plate, which include

Height

Weight

Total pixel in the object

Presence of characters

In the proposed system, the license plate is verified with the condition that the

proportion of maximum width and height is 0.1 to 0.25, and the total number of

pixels in the object is between 2000 to 8000 pixels.

In year 2005, Matas and Zimmermann had proposed a region-based license

plate detector in their paper which detects the linear combination of character-like

region as the license plate position. The detector will first decompose the image into

several threshold-separable regions that contains objects of interest. Machine

learning method is used in this detector to detect and classify the extremal regions

into relevant (character-like) or irrelevant region. This can be achieved by training of

the classifier with examples of regions, which consists of letters from given

categories such as font, digits, and capitals. The real license plate position is verified

as the longest linear spatial configuration between the spatial regions.

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One year later, Balazs Enyedi, Lajos Konyha and Kalman Fazekas used

horizontal and vertical analysis to locate the license plate from the image in their real

time number plate localization algorithms. In this algorithm, the region with highest

intensity variation that matches with the predefined window size is selected as the

license plate position. This is determined by the observations where the high contrast

area in a vehicle is usually the license plate area, for example, Malaysia license plate

with black background and white license characters. Besides, frequent changes of the

horizontal intensity with sharp variations can be detected when the letters and the

numbers presented in same vertical levels.

First, the system will first detect the intensity variation for each row. The

adjacent row that exhibits the biggest variations is selected as the region of interest.

Next, the possible horizontal level of the plate region is determined by the highest

variation area from the intensity variation of each row. After bandpass filtering, the

possible position of vertical edge for license plate can be determined by the row with

highest amplitude region. The lower boundary and the upper boundary of the vertical

edge is the first local minimal found from the highest magnitude. On the other hand,

the horizontal edge is detected by analyzing the average value of the curve. A

maximum distance between the characters in the license plate is estimated in

advanced where the possible area detected that are too close to each other are

merged. The real number plate position is selected form the license plate where the

ratios of the vertical and horizontal edge fulfill the predefined criterion.

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Table 1 Intensity histogram to determine the position of car plate

2.2 Character Segmentation

Character segmentation is the process of to separate the characters exist in

license plate into one character per area. The process is proposed in order to simplify

the character recognition process where the template used in analysis can be highly

decreased. However, there also exists system that discards this process, reorder the

segmentation and identification process using spationigtron model as recognition

method as describe in Real Time Car Plate Recognition Improvement system based

on Spationigtron Neural Network as proposed by Dariusz Król and Maciej Maksym

in year 2008.

Early in year 1994, a. Eun Ryun Lee, Pyeong Kee Kim, Hang Joon Kim in

their paper of Automatic Recognition of a car license plate using color image

processing use license plate histogram and the histogram of each color to segment

the plate region into foreground that consists of characters and background area

represents the space between the characters. Using this algorithm, the input image is

converted to four color groups, which is green, white, red, and otherwise group, the

horizontal and vertical color histogram of the four groups is calculated and a HV

(horizontal and vertical) histogram is plotted. The character region is extracted based

on the HV histogram by determine the histogram of each character’s color.

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Similarly, during year 2003, Choudhury A. Rahman, Wael Badawy, Ahmad

Radmanesh use horizontal and vertical intensity projection in their real time vehicle’s

license plate recognition system to segment characters exist in license plate. The

algorithm is based on the pattern matching towards the horizontal concentration of

colour. The effectiveness of the system varies with the font use as the template and

the font used on license plate where in this case, Arial font is used. Various

histograms of letters A to Z and digits 0 to 9 are stored as templates, which include

the histogram for:

Horizontal Histogram of: Vertical Histogram of:

Full size Full size

Lower half Left half

Upper half Right half

Lower one third Left one third

Upper one third Right one third

Lower one forth Left one forth

Upper one forth Right one forth

Upper two third

Table 2 List of Histogram created as templates

The obtained histogram of the license plate was then compared to the

template to determine the area of each character as shown in figure below:

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Table 3 Example of Character Extraction using horizontal and vertical intensity projection technique

Later in year 2006, V. Shapiro, G. Gluhchev, and D. Dimov use the concept

of adaptive iterative thresholding and analysis of connected component analysis of

license plate for character segmentation. In this system, a character clipper is

introduces to separate license plate region into isolated rectangular box containing

one character as the assumption that no overlapping exist between characters. The

algorithm of the character clipper is as bellows:

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Table 4 Chacracter Clipping algorithms

Table 5 Sequential Steps of plate segmentation with adaptive threshold = 3

At the same year, A. Banerjee, K. Basu and A. Konar in their paper of

Desigining a Real Time System For Car Number Detection Using Discrete Hopfield

Network propose a character segmentation algorithm based on vertical scanning on

binary image to search for stretch of white pixel. In this system, the gap or the space

between the consecutive characters is utilized to extract individual characters. Firstly,

vertical scanning is performed on the binary image to search for the stretch of white

pixels. A value that compatible to the dimension of number plate is set to examine

the stretch. The top position of the character is determined when the stretch exceeds

the preset value, and so for the bottom of number plate. Next, horizontal scanning is

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performed on entire image and the positions of the individual characters are extracted

according to the gap exist between consecutive characters.

2.3 Character Recognition

Character recognition is the most important process in the whole license plate

recognition system. The system cannot be applied in real world as vehicle

identification without the recognition process involved even though the position of

plate is successfully detected and the character in segmented from the plate region.

Throughout the various techniques used in the recognition process, template

matching and neural networks approach are most common used by the researchers

worldwide.

Template matching is a technique used to compare the character on the image

with the database for each possible character to find the best match. At year 1999,

Naito, T. Tsukada, T. Yamada, K. Kozuka, K. and Yamamoto, S had developed a

recognition method for inclined license plate using template matching approach.

Several sets of the templates are prepared in advanced with the same letters and

digits with different inclined angels as in figure below:

Table 6 Inclined templates designed

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According to the incline of the plate and the hypothesis formed by the

alignment of the characters extracted, appropriate templates are utilized for the

template matching process. On the other hand, Mei Yu and Yong Deak Kim in year

2000 also used template matching in their system of Korea car plate recognition.

Because of the uniform license plate specification presented in the system, template

matching is employed as relatively less computation is involved compared with

neural networks. The template prepared is according to the 15 region names and 70

usage codes used in old Korea car plate, and the corresponding 14 and 25 for new

license plate. The samples of template used are as follows:

Table 7 Korean License Plate Template

The template is divided into four, one for the first character, second for

second character, third for the usage code, and forth for digits 0-9. Each character

segmented is compared to the corresponding template set, and the template with the

smallest distance to the unknown character is regarded as that character.

In year 2006, A. Banerjee, K. Basu and A. Konar used Discrete Hopfield

Network, a modification of neural network approach for license plate recognition.

The system is implemented by converting the character matric into row vectors and

served as the input to the trained neural network. When there is perfect match found,

the value of the row vector is set by all zeros.

To achieve this algorithm, the maximum size of individual character is found

and the characters are mapped into the 2 dimensional matrices corresponding to the

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maximum size of the characters. The 2D matrices are then converted into row

vectors where the second row of the matrices is appended to the first row, the third

row to the second row and so on. Next, a neural network with neuron equal to the

size of the row matrix is designed. The system function by subtracting the row vector

of the nth input character with each of the row vectors corresponding to the nth

characters of the cars used for training of network. The resultant row vector will have

all zeros value for the perfect match between input pattern and training pattern.

When there is no perfect match detected, the difference between the input pattern and

training patterns is searched and the resultant vector with minimum nonzero terms is

treated as the matched character.

At year 2007, Clemens Arth, Florian Limberger, and Horst Bischof used One

Vs All and tree-like structure recognition base on Support Vector Machine (SVM)

classification for character recognition process. For one vs all approach, a k binary

SVM is trained where k-1 class having negative label while there exist only one

positive label. The target class is then determined by the largest value of the decision

function. On the other hand, in tree-like structure, the characters are divided

according to the shape of each character. The decision is made layer by layer until

the final character class. The example of the tree-like structure proposed is shown in

figure.

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Table 8 Tree-Like Structure Classification

CHAPTER 3

METHODOLOGY

3.1 Introduction

In this project, the real time license plate recognition and monitoring system

will be implemented based on two input videos taken at the main entrance of UTM

which represents the vehicles in flow and out flow. The video will be pre-processed

in advanced to extract the time frame images from the video input by rate of 5 frames

per second with the assumption that the speed of the vehicles is slow enough at the

entrance. The extracted image will go through various image morphology processes

to remove the unwanted noise presented in the image. Next, the position of the

license plate will be located by the rule-based candidate filtering algorithm. The

characters presented in the license plate area will be extracted by cropping the license

plate area vertically according to its width. The segmented character will then be

performed stroke analysis to determine the characters presented. Finally, the license

number recognized will be recorded into the database that consists of the information

for the registered vehicles and the inflow and outflow records for the vehicles. A

time stamp will be inserted to the database as a record of time when the vehicle

detected.

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In this project, the real time license plate recognition and monitoring system

will be implemented based on two input videos taken at the main entrance of UTM

which represents the vehicles in flow and out flow. The video will be pre-processed

in advanced to extract the time frame images from the video input by rate of 5 frames

per second with the assumption that the speed of the vehicles is slow enough at the

entrance. The extracted image will go through various image morphology processes

to remove the unwanted noise presented in the image. Next, the position of the

license plate will be located by the rule-based candidate filtering algorithm. The

characters presented in the license plate area will be extracted by cropping the license

plate area vertically according to its width. The segmented character will then be

performed stroke analysis to determine the characters presented. Finally, the license

number recognized will be recorded into the database that consists of the information

for the registered vehicles and the inflow and outflow records for the vehicles. A

time stamp will be inserted to the database as a record of time when the vehicle

detected.

Output

Input

Database system recording the exact time and flow of

vehicles in and out

Recognize characters extracted by stroke

analysis.

Extract time frame from video with 5 frames per second

Extract letters and digits from located

car plate area.

Link the car plate information to

database and save as records.

Insert time stamp to the database for each

car flow records.

Image pre-processing and location of

license plate from

Video of vehicles flows (in and out)

Table 9 Overall Process of the system

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3.2 Image preprocessing stage

The video input of this system is a true colour video formed by true colour

images. A true colour (RGB) image consists of three different layers of image

containing same information, which are in red, green, and blue value. Thus, it require

three times of memory space compared to a grayscale image. At the same time, the

processing of the RGB image had to be performed on the three layers which cost the

additional three times computations and the process will be three times slower than

the process on grayscale image containing same information. To effectively speed up

the license plate recognition system, grayscale image is selected throughout the plate

localization, character segmentation, and character recognition process. Therefore, a

grayscale transformation process is needed to transform the RGB image into

grayscale intensity image by eliminate hue ad saturation information in the image

while remaining the luminance. In this system, the transformation is according to the

formula below:

0.2989 * R + 0.5870 * G + 0.1140 * B

After the grayscale image is obtained, thresholding method is used to convert

the grayscale intensity into binary black and white image using global threshold.

Otsu method is used to determine the global threshold value used in the process

which has the minimum intra class variance between black and white pixels. After

thresholding process, the intensity value that larger than global threshold value will

be set to 1 (white pixels), while the otherwise to 0 (black pixels). The white pixels

will be treated as object candidates while the black pixels as background.

The binary image is the passed through different morphology process to

remove the unwanted information from the image and extract the useful information.

The morphology processes performed are as follows:

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Table 10 Sequence of morphological process performed

3.3 Plate localization

After preprocessing stage, the object of interest is cropped based on the

outermost parameters of the object, and the cropped objects are stored as license

plate candidate to be analysed. Plate localization using rule-based candidate filtering

approach will be performed. The candidate that does not fit the current analysis will

be removed whereas those satisfy the current analysis will be stored for the next

analysis. The analyses that involved are as follows:

clear border - remove the objects in image that

connect to image border

small pixel removal - remove the connected region with

very small object area

dilation - to connect character that close to each

other as one object

color inversion for special plate (taxi)

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Table 11 Process of rule-based candidate filtering

3.3.1 Dimension and compactness analysis

The dimension of the license plate candidate refers to the height and width of

the candidate area, whereas the compactness refers to the amount of white pixels

coverage in between the candidate region. The compactness of the candidate region

can be calculated using the formula:

Compactness=White Pixel AreaArea

Based on the observations upon few samples, the specifications of the height

and width of license plate are defined as follows:

Dimension Minimum number of pixels Minimum number of pixels

Height (H) 10 80

Width (W) 5 160

Table 12 Specifications of height and width of license plate

Dimention and compactness analysis

Ratio and angle analysis

Horizontal - vertical analysis

Candidate verification

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A license plate candidate pass the dimension and compactness analysis when

it satisfies one of the the following rules:

10 pixels < Height < 80 pixels and 5 pixels < Width < 160 pixels

Compactness > 70%

3.3.2 Ratio and angle analysis

The ratio analysis refers to the ratio of the width over height of the plate

region, and the angle analysis is perform on the angle in between the all the selected

candidates. A few assumptions are made when calculating the ratio of width over

height based on the empirical data obtained from the observations. The assumptions

are listed as follows:

For single line plate number, height < 40 pixels

Plate region with height > 40 pixels is assumed to be double line plate

number.

A single character inside car plate will have the width equals to ¾ of its

height.

The number of characters inside a plate region range from 3 to 14 characters.

The plate region with number of characters less than 3 is assumed to be part

of a double line plate number.

The analysis is divided into two algorithms, each for the single line plate

analysis (height < 40) and double line plate analysis. For height < 40 pixels, the

candidate pass the analysis when one of the rules stated below is satisfied:

The number of characters in between 3 to 14 characters.

The number of characters less than three and the angle between candidates

are 0,π (single line plate number) or ±π2

(isolated double line plate)

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On the other hand, when the height of plate is more than 40 pixels, the

candidate pass the analysis only when ration of width to height is greater than 0.75.

2.2.3 Horizontal-vertical analysis

In this analysis, the candidate will first be introduced to a thinning process.

The candidates that proposed only horizontal line after thinning process is assumed

to be non-plate candidates and removed from the candidate list. Next, the vertical

histograms of the remains candidates are generated using the formula below:

By analysing the characteristics of the vertical histogram obtained, the line

image and the non-vertically connected image is removed from the candidate list.

2.2.4 Candidate verification

The validity of the license plate candidates can be verified by computing the

angles of the object presented in the candidate region, which can be obtained through

the following equations:

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The candidate is verified as real plate region when the angle obtained is less

than 20o.

3.4 Character segmentation

After the location of the license plate is determine, character segmentation

process can be done to extract the characters from license plate area. This is to

provide the input of the character to the recognition algorithm as an individual

character with one pixel thick. In this system, the individual character is extracted by

crop the license plate region vertically based on the width of the plate region. The

algorithm begins with classification of the license plate into normal plate and special

plate, where special plate is defined by the cursive plate character in this process.

Next, individual character is segmented by crop the object vertically

according to its width. The extracted characters will be refined to remove small

portion of neighbouring object that being cut along. This is done by searching for

biggest object in the region and remove all other smaller objects exist within the

region. To avoid the tracing errors that could be occurred during the recognition

process, the extracted characters are resized to match the size of the defined template.

Last but not least, for some of the plate characters that consist of the combination of

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small and capital letters, the shifting process is required to shift the small character to

the centre of the cropped region.

3.5 Character recognition by stroke analysis

Stroke analysis is the most common method used in recognition of the

oriental writing system, such as Japanese, Mandarin, and Korean. This algorithm

trace the characters in a way similar to human handwriting, where the various stroke

that used to combine and form the character is identified. The algorithm consists of

two main processes, which are to trace character and to recognize character.

3.5.1 Trace character

Tracing a character in stroke analysis literally means to generate strokes

(sequence of movements during the tracing procedure) for each character. During

this process, stroke is defined as a direction structure which refers to the combination

of numbers corresponding to the direction of current pixel to target pixel. The

direction structure used throughout the program is as shown in figure below:

Figure 12 Direction structure

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The tracing algorithms start by finding the first pixel of the extracted

characters. Generally, the first pixel is determined by through the image in left to

right and top to bottom sequence. The white pixel encountered first is treated as first

pixel. However, for some other characters such as A, 2, and 7, the first pixel does not

follow the general rules. Therefore, first pixel refinement is needed. It is done by

scanning through the image that divided into top and bottom half, and the white pixel

with only one neighbour that not located right at the border is treat as first pixel. Next

scanning process is performed starting from the first pixel trace to neighbour pixels

and the value read is store in a chain code variable in the form as below:

Figure 13 Chain code structure

The 0 indicate the end of the stroke and the coordinate refer to the starting

coordinate of each corresponding stroke. This variable corresponds to all directions

of the movement when tracing a character.

3.5.2 Recognize Character

Character recognition is the process to match the chain code of the input

character with every single character template. The recognition algorithm start by

assigning the chain code that is out of track or no match with the template to ‘0’ for

digits and ‘-’ for letters. After this, the chain code is decoded in the form of stroke

array where next stroke is determined whenever a 0 is encountered in the chain code

variable. The first stroke is analysed first starting with the start coordinate associated.

The recognition process is done by comparing the direction sequence to the available

template. The process is repeated until the last stroke in the character is trace. The

character and template is said to be matched only when there is no out of track pixels

exist in the whole recognition process. If there is no matching, the recognition

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process will continue to next character’s template until the whole temple library is

scanned.

3.6 Database Creation

After recognized the license plate numbers, the result will be used to update

the database system created by connecting the database created with the program. A

time stamp will be applied to the record indicating the time for the record taken.

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REFERENCES

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