mathematical modeling and signal processing technique in ... · transportation system is a mass...

67 Dr.Manish Kumar Saini,Shilpi Dhingra,RajvirSingh

International Journal of Electronics, Electrical and Computational System

IJEECS

ISSN 2348-117X

Volume 4, Special Issue

May 2015

Mathematical Modeling and Signal Processing Technique In

Automatic Number Plate Recognition

Dr.Manish Kumar Sainia,Shilpi Dhingra

b,RajvirSingh

a,b

a,Electrical Engineering Dept., DCRUST,Murthal, Sonipat, Haryana 131039

bCSE Dept. , DCRUST, Murthal, Sonipat, Haryana 131039

ab CSE Dept.,,DCRUST,Murthal, Sonipat, Haryana 131039

Abstract

Automatic Number Plate Recognition is a special form of Optical Character Recognition(OCR) ,use

to extract number plate/standard plate from the vehicle image or sequence of image(in case of video).

Main aim of this system is to collect information of a car on the basis of License Plate (LP) only.

License plate detection and character segmentation are the critical phases of this system, detection

involves extraction of number plate from car image, this phase provide major contribution in the

success of ANPR, vehicle speed, plate size, normal text (other than number plate) or stickers,

environmental condition and another noise effects like uneven image, dirty plate, shadow etc are the

major problem of this phase. Segmentation involves separating character from acquired number plate

algorithm used for this phase should overcome problem of LP detection for the better performance

of LP character recognition phase. Paper presents the comprehensive review of license plate

recognition techniques and comparison of various paper on the basis of Global Image information

,Textual Feature and Artificial Intelligence and review also contain techniques used for license plate

detection, segmentation and recognition and country/region information for which respective

algorithm were designed, database used to test the algorithm with image resolution, processing time

and accuracy.

Keywords: License Plate Recognition(LPR); Global Image Information ; texture feature ; Artificial

Intelligence

1. Introduction

High definition images and videos and identification of object(from captured camera images)

provide an opportunity in image processing as well as in pattern recognition38

as well as in

Intelligence transportation system(ITS) ,ITSs include intelligent infrastructure systems and

intelligent vehicle systems40

, ANPR also known as ALPR acts as a backbone of Intelligence

transportation system is a mass surveillance method and used to identify vehicle by using its number

plate without human interference and use to address trafiic problems. ANPR was invented in 1976 in

UK developed to identify the stolen car ,ANPR was observed successfully first in 1978. ANPR has

several applications these application reached from complex real time environment to simple area

like car parking34

,Variable message signs1, Vehicle Access control1

,highway/electronic toll

collection35

borders and custom security, premises where high security is needed, like Parliament,

Legislative Assembly2, stolen car detection

25 and so on because of these application its value of

research is increased day by day as the number plate standard is region/country specific so,

complexity of this work varies from country/region to country/region/others (country by country).

ANPR is a mature yet imperfect technique, extensive work has been done in this field despite of that

system suffers from accuracy, precision and time consumption (like/type) problems because of

different styles of LP and region wise format and small LP region with different font size and color,



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May 2015

require some improvement and advance technology which attracts researcher interest and (that’s

why) its value of research is increased day by day.(it has received lot of attention from the

researcher) .There are of two types one is Online and another one is Offline ANPR system, Online

ANPR system works on real traffic video and images localization and interpretation take place

instantaneously in contrast, Offline ANPR system stores captured images or video in centralized data

server and used it for identification of LP3.

2.1 Phases of ANPR: The ANPR system composed of four stages fig. 1 shows the structure of the ANPR process. The

performance of an ALPR system depends upon every stage. There is no need to install a hardware on

vehicle for this system.

Fig1. Frame Work of ANPR System

The first phase is to acquire the car image using a camera. Camera resolution, shutter speed,

orientation, and light, are the various parameters of the camera, ANPR can either use black and white

or infrared camera to take an image. Camera preserve object details at a longer distance .The second

phase is to detect the license plate from the image, this is most complex phase whole system

performance is bend on it because of complete environment conditions, extraction is performed

from whole image or video frames and by using some features, such as the boundary, edge, color

etc. Segmentation phase help to separate character from the recognized LP by template matching.

The third phase helps to separate character from the recognized LP by template matching, back

ground and fore ground color. The fourth phase is to recognize the extracted characters by template

matching or by machine learning , such as neural networks , support vector machine (SVM), Genetic

Algorithm4. Paper is organized into three major section includes Global image Information, texture

feature and Artificial intelligence and there is a comparison of various techniques based on License

plate detection(LPD), License Plate Segmentation(LPS), Character Recognition(CR) with their

respective accuracy and also on the basis of language.

2. Literature Review

Global Image Information includes local features which are useful for rough segmentation of the

object 31,

it is used in recognition and detection phases of LP.Texture Feature includes detection of

number plate base on color, edge/boundary basis, good for high density regions. Technique such as

Neural Network (NN), Fuzzy Classification, SVM used to trained the ANPR system comes under

Artificial Intelligence(AI). Review also included a comparison table based on the phase wise

technique of the system, image resolution ,static and video images and also mentioned accuracy of

the system.



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2.1. Global Image Information

Ashtari et al.,5 proposed a system designed for number plate location and image improvement using

modified color Template Matching Technique by analyzing hue of the color pixel and decision tree

with SVM homogenous fifth degree kernel (histogram equation and Laplace filter) were applied

respectively. In this pixel transformation like Fourier, wavelet and houge were ignored which

reduced response time. Results were measured in four different situations for speed control, low

resolution image detection rate and system overview performance were 96.8% and 94.4%

respectively, better than fuzzy logic but less than edge detection and moving window and Neural

network detection and recognition methods. Database of 1150 images were taken and this system

had been proven to be real time practice but not good for foggy and rainy environment and broken or

smeared LP and its system performance can be further improved. Massoud et al.,7 proposed a LPR

system in this extraction, recognition and database communication performed by using pre-image,

statically template matching and database servers respectively, pre-image was further divided into

detection and segmentation Identification, accuracy of LP was 91% for 100 patterns under several

condition using video steaming and it could be apply for the real time application. Overall accuracy

was reduced by 9% because of dirty plates, overlapped by vehicle or slant LP which can be further

improved. In13

Proposed an efficient algorithm of locating and extracting license plate and

recognizing each segmented character by a method based on morphological operations for the

elimination of non-plate region, connected component Analysis and template matching respectively.

Algorithm was subdivided into four parts- Digitization of image, Edge Detection, Separation of

characters and Template Matching and applicable to Indian LP. Algorithm was tested by 50 color

image with size of 640 x 480. By compensating the brightness distortion and chromaticity distortion

and improving ambient lighting conditions can improve performance of the system. In19

author

proposed vehicle detection method based on And-Or Graph (AOG) which decompose object into

multiple parts and Hybrid Image Templates (HIT) to detect no vehicles and vehicle region.

Experiment performed using precision recall curve on total 240 images in that curve proposed

method was better than AOGI method by Li et al., 201332

and also performed well in case of various

vehicle poses. Proposed method used edge information, the texture, color, and flatness information

this made it more efficient .The combination of AOG and HIT improves the detection accuracy in

complex urban traffic conditions with occlusion among vehicles and between vehicles and non-

vehicle objects and method also adapted to various vehicle poses and shapes. But only day traffic

conditions and front view of the vehicles were considered and also low detection cannot allow this

algorithm for real time traffic system. Author proposed an approach in21

to solve the OCR problem

using intelligent template for matching (ITM) for Argentinean license plates. To resolve training

required of ANN used for OCR, ITM obtained the performance in time, accuracy and in case of ITM

with no prior training made it flexibility and simplicity of original template

2.2. Texture Feature

Proposed algorithm based on LP localization in this one dimensional plate signal generated to

extract LP from image which contain five statistics (i.e., strength of the signal, normalized maximum

amplitude, frequency of maximum amplitude, frequency center and frequency spread) were

determine using statistical analysis of Discrete Fourier transformation with color-based histogram

threshold with the concept of periodicity of characters which made Fourier efficient, Besian

approach was used to classify plate region with overall accuracy of the system of 88.1% for public

data set greater than 3.3% from the concept of Visual words(PVW) proposed by Zhou et al.(2012)49



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with 84.9% accuracy and also its processing time was <150ms to competitive values which made it

capable to implemented in real time system but in the cost of high computational complexity as

compare to other model6.In this a license plate detection system was implemented for extracting

vertical edge , candidate region and segmenting the plate region used Sobel operator, morphological

filtering and some geometrical feature respectively The Iranic number plate experimental results

achieved appropriate performance in different scenarios with average accuracy was above 92%

therefore proposed system was reliable and also practical because of the low computational cost

algorithm be used for different countries8. Author presented License plate location method in this

Gaussian filter and Histogram Equalization used for preprocessing and Wavelet transformation

which generate wave crust for LP position and EMD analysis involved projection reconstruction &

Hilbert Transformation applied to choose desire wave crust for LP. Algorithm was capable to deal

with several exacting condition with better performance against a sliding window technique by

Wang48

(2011) whose accuracy was 97.33% ,overall execution time 0.58-0.62 sec. A database of 765

images (640 x 480) was taken. It was highly adaptable to non linear and non stationary data but one

LP detected at a time this problem can be resolve further 10

. In11

Author designed a Character

Recognition method based on SIFT feature point clustering and central matching technique by three

points which was the improved version of Zahed and Salehi algorithm (2011)[47] used only SIFT

feature for LP recognition. This algorithm was designed for Chinese LP and for best results affine

vertical distortion were applied, database of 885 images with resolution range from 30x45 to

112x200 were taken with success rate of 97.6% .Invariant characteristics of SIFT feature helped to

improve recognition. Color information of a character can enhance the performance of this

work.In[12] author described LP detection method based on gradient information and cascade

adaboost detector image preprocessing, LP detection and LP conformation were the main method of

this system and false positive(because of adaboost) was reduced by heuristic judgment and voting

based method trained by SVM. Database of 4087 image with image resolution of 1920x1088 recall

and precision calculated by evaluation protocol was 87.29% and 62.31% respectively, High recall

value helped to handle multiple LP simultaneously. Threshold value was 0.5, scale factor of

cascade adaboost detector was chosen artificially as a result LP may not scanned properly this

problem can be resolve further with increase precision and recall values. In14

, A novel method

proposed for LP detection and tracing by Cascade AdaBoost Detector (CAD) verified by HOG

classification and Tracing Learning Detection(TLD) respectively using video frames.CAD also

helped to maintain tracing list and trained by 78 x 22 images size, CAD result verified by HOG

classification and tracing was decompose into tracer, detector and learning. Result was measured in

the form of recall, precision and f-measure rate. Video image is blur as compare to still image but

contain more information, CAD applied on new LP and helped to increase performance and TLD

helped to reduce false positive this algorithm can be extent further. In[16] paper author described a

rear-view vehicle detection and tracking method used salient parts of multiple vehicle, detected parts

of vehicle considered as graph nodes help to construct Markov random field model (MRF) and

loopy belief propagation (LBP) was used graph to obtain the vehicle locations. Kalman filter helped

to track estimate vehicles’ trajectories. Recall, Precision and Mostly Tracked Trajectories (MT) for

sunny day time during heavy congestion were recorded as 95%,85.7% and 89.6% respectively. This

method adapt to partial occlusion and various lighting conditions and achieve real-time performance,

more salient parts, such as the windshield and front cover can be consider further to increase

performance.In15

Author proposed LP detection fusion technique of AdaBoost, color checking and

SVM detector. Test results of 950 real-world images shown that the fusion reduced the false alarm



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rate. The proposed Fusion detector performed better results from the conventional Adaboost detector

throughout the ROC (Receiver Operating Characteristic) curve. The AUC (Area Under the Curve) of

fusion reached 0.9081 but for Adaboost detector it was 0.8441, which showed that the modification

on feature extraction and the multistage information fusion significantly improve the LP detection

performance. Fusion detector was designed for detecting Chinese LP, it can designed for detecting

the LP in other countries after modifications of the color checking module and changing some

classifier and some features for adaboost. Author proposed in17

LPR system LP detection,

segmentation and Recognition using Cascade Adaboost Detector, improved bolb exaction and OCR

methods respectively. Database were collected from video data set by global edge and harr like

feature including classification of six layer (two layers based on global statistical feature and

remaining four trained by Cascade AdaBosst detector) global layer help to reduce non plate region.

Overall accuracy of the system was 94.03% greater than its active contour Guo Jing-Ming et al;46

(2011)i.e., traditional CCA whose overall accuracy was 74.5%. It was based on real time

environment and overall accuracy can be improved further. Author proposed methodology based on

color based method instead of text based and used characteristics of color combination for color edge

extraction and searching, location principle was applied. Edge extraction was the main part of the

algorithm and used trigonometric imaging and color discrete characteristics to cover weaken and

unfavorable factors. Algorithm performed well in complex scenes and can process 11 categories of

LP with different color combination and its efficiency was higher than text based methods overall

success rate and execution time was 94.7% and 57 ms respectively18

.In 22

Author proposed a method

in which some improvement had been done by improving template matching OCR method which

included font type, template resolution, noise, tilting and the painting, as a result some improvement

in accuracy had been measured in Indonesian LP. The result was increased by from Jaccard et al.,

(2008)45

similarity by21

.67 percent and the reduced of number of noise detected by 99.04 percent,

including more samples can further increase the existing accuracy. In 27

paper author used computer

vision technique to perform automatic recognition of a car for garage opening and used single object

detection framework technique to perform detection and recognition of a car in 42ms and 22 ms

respectively for every single car. Detection procedure is trained with public data set. Feature-based

object detection algorithm can further increase efficiency of the system for detection and recognition

of cars and texts. Wang et al.,29

proposed an LPL method based on Discrete Wavelet Transformation

(DWT).DWT was applied on grey image and its HL and LH subband were used to extract vertical

and horizontal information respectively vertical accuracy achieved by removing insignificant

coefficient. Algorithm was tested on both indoor and outdoor environment and different size of LP

.This algorithm did not perform well if the color of background and license plate matched, accuracy

of the system was noted as 97.33%. Algorithm achieved real time LPL system. Paunwala et al.,[34]

proposed an algorithm for LP detection , algorithm is subdivided into three stages grey scale

conversion then morphological operation followed by connected component analysis, It can detect

LP from complex environment condition such as shadow, dirty or blurr images and also perform

well for multiple LP or video sequence .success rate of whole system was noted as 98.8% for single

and 95% for multiple LP. Overall accuracy can be further improved for video image.In38

author

proposed a novel idea for LPD namely Operator Context Scanning (OCS) applied to sliding

window analysis help to reduce processing time of high resolution images and increase the ANPR

performance in terms of speed, Concept of “context’’ of the image according to an operator”

categorize the image on the basis of environment condition. Algorithm was tested on various

environment conditions. This algorithm performed well from their previous work in39

System



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capability can be increase further by binarization capabilities for OCS ,better sensor system for noise

free image and also by mainitaing good database.Chen et al.40

proposed a LP recognition method in

this algorithm localization performed by using salience features and after segmentation feature

salience classifier applied fer recognition of characters , algorithm performed well in various

environment conditions and tested on 1176 image and over all accuracy reached to 95.7% , its

computational cost was less than standard SVM, accuracy can be further improved for blazing light

region.

2.3. Artifical Intelligence

In 23

, author proposed LP detection method a novel binary shadow removal Bersian algorithm with

Gaussian filter and image gray equilibrium and feature extraction elastic mesh and SVM used for LP

detection, segmentation and recognition respectively. Database of 9026 images include

Kana,Chinese and English character were used and overall accuracy of the system was greater than

7.53% from fuzzy discipline and Neural Network. This technique cannot recognize white character

on black plate. Author proposed a hybrid algorithm included the feature of multithresholding and

Neural Network technique with artificial neural network for segmentation and recognition

respectively, neural network was trained by neural training, validation and testing for preprocessing

median filter was applied. Algorithm recognition rate was greater than 1.66% to competitive

algorithm which used NN and image co-relation by K.Yilmaz (2011)44

. This algorithm helped to

improve the quality of images, detect the characters or digits and license plates realization at an

augmented level of certainty. Increasing recognition rate and reducing the complexity of Neural

Network are the further scope of the paper9. In

20Author designed a system for Indian Standard High

Security Number plate with a neural network for character recognition was trained to recognize all

the characters ,Image of a vehicle with Indian standard number plate was the input of the system and

produced the characters displayed in the form of computer data as its output. System was adaptive

and gave satisfactory results in case of slight variation in the same characters due to noise. The

system threshold was found to be 0.85 which can be further improved on training .In28

author

proposed a novel idea for LP segmentation and character recognition input of this system was a LP

or a band containing LP information. Gaussian filter, an innovational Laplacian-like transformation

and a median filter performed as preprocessing made system robust to non-uniform environment

conditions, segmentation was performed by indigenous and relative features of character image set

character size by 40x40 pixels after that principle component analysis (PCA) was applied for image

recognition followed by two feed-forward neural networks with one hidden layer algorithm was

tested on 120 Iranian LP average success rate for segmentation and CR were noted as 94% and

90.5% respectively with execution time of 0.55ms. In36

author proposed an enhanced version of LPD

given by their previous work in (Deb et al.,37

), for Korean number plate for LPD author used

density based region growing and sliding concentric window and ANN applied for segmentation and

recognition respectively, algorithm was tested for different view point, illumination conditions and

performed well in all these cases. Working with blur image is the future scope of this algorithm.

Krishnakumar40

designed an ANPR system on android mobile phone ,grey scale conversion and

Gaussian filter applied for preprocessing ,OCR used for segmentation and recognition and database

were trained by ANN. In this algorithm processing time is proportional to the size of the input

images. Future scope of the algorithm is to use it for multinational car LP. In 30

author proposed a

new genetic algorithm (GA) with two new sorting based crossover operator with flexible fitness

function to differentiate number plate text from normal text and increase the convergence speed of



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the system to identify the location of license plate. Algorithm is interdependent to shape, color and

size of number plate can detect plate of varies region. Geometric Relationship Matrix (GRM)

concept had been introduced for the identification of different region LP, overall accuracy of the

system was 98.4%. Algorithm can performance well in real time environment.

Table 1. Performance Comparison of some ANPR systems S

N

O.

Reference

Number

Technique used

Database

Used with

image

resolution

Plate Format

Accuracy of

the system

Remarks

1 Ashtari et al.,5 CR: Template

Matching

Technique

1156 Iranic LP da: 96.8%

ova:94.4%

performance can be further

improved for foggy and

rainy environment

2 Al-Hmouz et

al., 6

LPD: Discrete

Fourier

Transformation

1758 images

(640x480)

- ova:88.1% By reducing computational

complexity this algorithm

can be implement in the

real time environment

3 Massoud et

al., 7

PR: pre-image,

CR: statically

template

matching

Video Egypt LP ova:92% overlapping of LP problem

can be further improved

4 Lalimi et al.,8 LPD: Sobel

operator,

morphological

filtering

425 Iranic LP ova:92% This algorithm can be used

for different countries

License Plate

5 Bhushan et

al.,9

LPS:

Multithresholdin

g

CR: Neural

Network

technique with

artificial neural

network

- Indian LP cr: 98.4% Neural Network

complexity can be further

improved

6

Shouyuan et

al., 10

PR: Gaussian

filter and

Histogram

Equalization for

preprocessing

LPD: Wavelet

transformation

with EMD

analysis

765(640 x

480)

Chinese LP

as well as

other

countries LP

et:0.58 sec

ova: 97.91%

Algorithm can be further

improved for multiple LP

detection

7 Chen et al., 11

LPR: SIFT

feature

885 (30x45)

to (112x200)

Chinese LP ova: 97.6% By introducing more SIFT

and color information

detected LP performance

would be increased

8 Wang et al., 12

LPD: Gradient

information and

cascade

Adaboost

detector

4087

(1920x1088)

Chinese LP P:87.29%

R: 62.31%

Performance can be

improved by selecting

threshold automatically

and reducing false positive



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9 Dey et al., 13

LPD:

Morphological

operations,

LPS:CCA

CR: Template

matching

respectively

50 (640 x

480)

- - brightness distortion and

improving ambient lighting

conditions can be further

improve

10 Wang et al., 14

LPD: Cascade

AdaBoost

Detector

Video

image(78 x

22)

Chinese LP R:0.78755

P:0.74808

CAD and TLD helped to

increase LP detection rate

11 Zhenjie et al., 15

LPD: AdaBoost

and

SVM

950 Chinese LP P:0.9081

R:0.8441

Adaboost with SVM

increase de tection

efficiency

12

Tian 16

LPD: MRF and

LBP

1777

(2592*1926)

- P:85%

R:95%

MT:89.6%

Salient parts, such as the

windshield and front cover

can improve system real-

time performance

13 Zheng et al., 17

LPD: Cascade

AdaBosst

Detector

LPS: Improved

Bolb Extraction

CR:OCR

Methods

586(video

clips)

(648*486)

- et:0.2 sec

da:96.4%

sa:98.82%

ca:98.7%

ova:94.03%

Based on real time

environ-ment and better

than traditional CCA

algorithm

14 Yang et al., 18

LPD: Color

based extraction

,denoising and

searching

1384(600

text

image(600*

300)+784(7

86*576)

China(also

tested on

104

countries)

et :57 ms

ova:94.7%

This algorithm is not

country specific and

performed well in complex

scenes

15 Gazcón et al., 21

LPR :Intelligent

template

matching for

OCR

531

Argentine

LP

et :800 ms

ova:87.53%

Testing by efficient

strategies for the

evaluation of character

features provide better

scope to algorithm

16 Hidayatullah

et al., 22

Used salient

parts of multiple

vehicle detection

29 (different

sets of

images)

Indonesia

LP

ova:87.96%

Reduces noise problem of

LP.

17

Wen et al.,23

LPD: Improved

Bersian

algorithm

connected

component

CR: Support

Vector Machine

9026

English,

Chinese

numerical,

kana

ova:93.54%

da:97.88%

ca:97.16%

et:0.284sec

white character on black

plate recognition provides

more scope to the

algorithm

18 Ching-Liang

Su et al.,24

ring-to-line

mapping

technique

120 images - - Unique Ring-to-line

mapping technique provide

greater efficiency



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19 S.Namitha et

al.,26

LPD: adaptive

histogram

equalization,

CR: template

matching and

fuzzy classifier

-- Indian LP --

20 Wijnhoven

and Peter 27

computer vision

technique for

card detection

360 images -- ova:90%

et:300 ms

over all processing time is

300 ms can be reduce

further

21 Sedighi and

Vafadust28

LPS: used

indigenous and

relative features

of character

CR: Two feed-

forward neural

networks with

back-propagation

120 images Iranian

license

plates

et:0.55ms

sa:94%

cr:90.5%

Algorithm is robust to non-

uniform environment

conditions

22 Wang et al.,29

LPL: Wavelet

transformation

300

images

(400 * 300)

--

et:< 0.2 s

Ova:

97.33%

Proposed algorithm can

extract LP from front as

well as from back

.Applied in real time

application

23 Samra and

Khalefah30

LPD:Genetic

algorithm with

GRM

800 images

Applicable

to different

country LP

Ova:98.4%

Algorithm is

interdependent to shape

color and size of number

plate can detect plate of

varies region

24

.

Neto et al.,33

LPD:CCA ---- ---- ----

---

25

..

Paunwala et

al.,34

PR: RGB to gray

conversion

followed by

contrast

enhancement

LPD:

mathematical

morphology and

component

filtering

750 Images

Indian LP

ova: 98.8%

( single LP)

95%

(multiple

LP).

Algorithm can detect

single as well as multiple

LP

26

.

Deb et al., 36

LPD: sliding

concentric

windows (SCW)

CR: artificial

neural network.

120 image

(640*480 )

Korean LP

da: 89%

Algorithm can detect and

recognize number plate in

in various weather and

lighting condition



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27

.

Giannoukos

et al38

LPD:window

analysis, namely

Operator Context

Scanning

Video

(tested on

1428

images)

Greek LP

Ova: 90.9%

(still

images)

Ova: 88.1

%(video

+still)

Algorithm reduce

performace cost by sliding

windows pixel operators

and OCS increase system

speed

This OCS method

increases the

processing speed of the

original SCW method by

250%.

28

.

Chen et al.,40

CR: feature

salience

Classifier

1176 images

(640 × 480

pixels)

Chinese LP

da: 97.3%.

cr: 95.7%

ova:93.1%

29

.

Krishnakumar43

PR:Gaussian

filter

LPR:OCR

420x312 -- -- Algorithm is implemented

for android mobile phone

PR: Preprocessing, LP detection/localization (LPD/LPL), LP Segmentation (LPS), Character

Recognition(CR)

ova: Overall accuracy, da: LPD accuracy, sa: LPS accuracy, cr: CR accuracy, p:precision, r:recall, et:

execution time.

3. Comprehensive Analysis:

This paper presents major reported research articles on an ANPR systems, ANPR/ALPR consists of

four main phases as in Fig. 1 output of every phase of ANPR system effects on its lower phase

efficiency. Image acquisition can be captured either from CCTV ANPR cameras which are fitted on

road side these camera are generally connected with huge database maintained for traffic control,

police enforcement, toll collection, public sector etc. or camera can be of android phone43

or simple

camera with limited memory for normal application like small car parking zone, garage opening27

,

Information can be taken in the form of images or video7,14,38

and further processed (in lower phases),

next phase of this system is LPD and it impacts the accuracy of whole ANPR system and this phase

deals with the complexity of the whole system in this extraction of license plate is performed from

whole image or video frames which will further segmented and recognized, some ANPR system

highlighted in literature considered only limited environment condition 5, 36

only few works in real-

time environment11-12

, license plate generally suffer from partial occlusion4, shadow, irregular

illumination conditions, variable distance41

, scene angle on frame41

, foggy and rainy5, multiple

license plate, number of frames in video problems.

Algorithm discussed in 22-23,34

worked for shadow, blur images and multiple LP detection but at the

cost of high execution time. Most of the algorithm are region or language dependent 5,7,11

but some of

them are applicable to all region or major countries discussed in10,18,30

. Algorithms are applied for

detection phases are Sobel filter8, Discrete Fourier transform [6], wavelet transform

29, wavelet with

EMD analysis10

, cascade adaboost detector 12,15,17

and various color based methods5. Segmentation

phase help to separate character from the recognized LP by template matching, back ground and fore

ground color of LP or by reducing character confusion such as (0 o),(v w), (I 1) etc. method such as

multi-thresholding9, CCA

13, Bolb extraction

17 etc. are used for segmentation. Recognition phase is

probably last but its results provide accuracy to whole ANPR system, generally combined algorithm

is designed for recognition and segmentation phases. Most of the algorithm in this phase is based on



IJEECS

ISSN 2348-117X


May 2015

machine learning where various pattern of machine are trained by number plates, which include

SVM5, 12, 40

. Fuzzy logic26

, genetic algorithm30

, neural network9,20,28,36

, OCR methods17,21

, SIFT

feature11

etc. output of this phase is digitalized image which will further processed according to the

system requirement.

.

4. Conclusion This paper presents a comprehensive review of existing ALPR/ANPR system techniques. Global

feature provides local features representation which are useful for the rough segmentation of the

object, it also provides information which is used for discrimination of class for recognition and

detection phase of LP, Textual feature information used to detect number plate base on color, edge

and boundary information , this type of feature uses the presence of character in LP it is effective in

high edge density area .Artificial Intelligence is the intelligence exhibited by software or used to

train the system for identification of number plate, it includes NN, SVM and OCR methods. The

future researcher should concentrate on reducing processing complexity to work with real time

scenario, video based ANPR, increasing the recognition rate of ambiguous character like (0 O),(v

w),(I 1) etc., designing a cost effective ANPR system or considering all real-time environment

including indoor or outdoor condition, generating an ANPR system for local region and for

multinational car license plate.

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