Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
13,
No.
1,
January
2019,
pp.77
–
84
ISSN:
2502-4752,
DOI:10.11591/ijeecs.v13.i1.pp77-84
77
A
utomated
P
arking
Management
System
f
or
Identifying
V
ehicle
Number
Plate
Asha
Singh
1
and
S.
Prasanth
V
aidya
2
1
M.T
ech
Scholar
,
Gayatri
V
idya
P
arishad
Colle
ge
of
Engineering
(A),
India
2
Department
of
CSE,
Gayatri
V
idya
P
arishad
Colle
ge
of
Engineering
(A),
India
Article
Inf
o
Article
history:
Recei
v
ed
Jun
20,
2018
Re
vised
Aug
21,
2018
Accepted
No
v
16,
2018
K
eyw
ord:
License
Plate
P
arking
bill
Recognition
T
emplate
Matching
ABSTRA
CT
By
using
image
processing,
the
Automated
parking
management
system
(APMS)
to
rec-
ognize
the
license
plate
number
for
ef
ficient
management
of
v
ehicle
parking
and
v
ehicle
billing.
It
is
an
independent
real-time
system,
reduces
number
of
people
in
v
olv
ement
in
parking
areas.
The
main
aim
of
this
system
is
to
automated
payment
collection.
This
(APMS)
system
e
xtract
and
recognize
license
plate
num
bers
from
the
v
ehicl
es,
then
that
image
is
being
processed
and
used
to
generate
an
electronic
bill
.
Generally
in
the
parking
lots
hea
vy
labor
w
ork
is
needed.
This
system
used
to
decrea
se
the
cost
of
the
labor
and
also
enhance
the
performance
of
the
APMS.
This
system
is
composed
of
v
ehicles
license
plate
number
e
xtraction,
character
se
gmentation
and
character
recognition.
A
proper
pre-
processing
is
done
before
e
xtracting
the
license
plate
and
it
also
generates
the
entry
time
and
e
xit
time
of
the
v
ehicle
and
finally
generates
the
electronic
bill.
Copyright
c
2019
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
S.
Prasanth
V
aidya
Assistant
Professor
Deartment
of
CSE,
GVPCE
(A),
V
isakhapatnam.
+91-9652733636
v
aidya269@gmail.com
1.
INTR
ODUCTION
In
image
processing,
computer
vision
and
pattern
recognition
algorithms,
se
gmentation
is
most
important
and
basic
step
[1,
2].
Se
gmentation
of
image
is
the
first
step
which
has
lar
ge
application
in
the
fields
of
robotics,
automations,
satellite
imaging
and
license
plate
recognition
[3,
4].
No
w-a-days,
the
license
plate
recognition
is
widely
used
in
the
management
of
traf
fic
to
recognize
a
v
ehicle
whose
licensor
violates
the
traf
fic
rules
and
it
also
helps
in
finding
the
theft
v
ehicles
it
doesn’
t
needs
an
y
manual
w
ork.
In
this
system
when
the
v
ehicle
i.e.,
car
enter
s
in
the
parking
lots
a
digital
camera
with
sensor
is
fix
ed
and
license
plate
recognition
system
is
recognize
a
license
plate
number
of
the
specific
car
.
It
also
enters
the
car
details
and
entry
time,
at
e
xit
time
it
automatically
calculates
the
parking
price.
It
is
the
most
suitable
and
ef
ficient
w
ay
to
a
v
oid
the
labor
w
ork
[5,
6,
7,
8].
The
rest
of
the
paper
is
or
g
anized
as
follo
ws.
Analysis
and
Study
of
System
are
discussed
in
Section
2.
In
Section
3,
design
of
P
arking
Management
System
is
discussed.
Methods
used
in
the
proposed
method
are
gi
v
en
in
Section
4.
The
proposed
automated
parking
management
system
is
gi
v
en
in
Section
5.
In
Section
6
e
xperimental
results
are
presented.
Section
7
concludes
the
paper
.
2.
AN
AL
YSIS
AND
STUD
Y
OF
SYSTEM
2.1.
Existing
System
No
w-a-days,
parking
places
depended
on
labors
[9].
The
y
need
to
maintain
data
of
all
the
v
ehicles
by
ph
ysically
entering
the
information.
It
includes
high
prices.
Disadv
antages
are
the
Precious
time
w
asted
due
to
the
incon
v
enient
and
inef
fecti
v
eness
at
parking
places
and
more
consumption
of
fuel
while
idling
or
dri
ving
around
the
parking
places
[10].
J
ournal
Homepage:
https://www
.iaescor
e
.com/journals/inde
x.php/IJEECS
Evaluation Warning : The document was created with Spire.PDF for Python.
78
ISSN:
2502-4752
K
omarudin
et
al.
[3]
designed
and
analyzed
the
license
plate
identification
through
a
digital
images
using
desktop
peripheral
and
binary
calculation
methods
using
adapti
v
e
threshold
and
global
threshold.
K
ongur
gsa
et
al.
[11]
proposed
real-time
intrusion,
detecting
and
alert
system
by
image
processing
techniques.
Y
iman
et
al.
[7]
aimed
to
solv
e
the
problem
of
identifying
the
v
ehicle
license
plate
number
at
the
parking
lot.
W
en
et
al.
[12]
proposed
license
plate
recognition
on
the
basis
of
a
no
v
el
shado
w
remo
v
al
technique
and
character
recognition
algori
thm.
Using
a
binary
method,
it
remo
v
es
the
shado
w
within
the
image,
which
is
based
on
the
impro
v
ed
Bernsen
algorithm
combined
with
the
Gaussian
filter
and
for
character
recognition
SVM
inte
gration
is
used.
This
system
also
consists
of
the
impro
v
ed
techniques
for
image
tilt
correction
and
image
gray
enhancement.
Generating
the
parking
bill
in
parking
slots
and
toll
g
ates
in
highw
ays
has
become
major
problem.
One
of
the
solution
is
to
propose
an
automated
license
plate
recognition
system.
There
are
numerous
recognition
systems
a
v
ailable
which
are
designed
using
dif
ferent
methods
b
ut
still
some
features
are
to
be
e
xplored
lik
e
v
ehicl
e
speed,
dif
ferent
en
vironment
conditions
can
ef
fect
the
system
recognition
rate.
The
proposed
system
has
o
v
ercome
the
dra
wbacks
of
the
e
xisting
system.
2.2.
Pr
oposed
System
T
o
r
educe
the
in
v
olv
ement
of
man
po
wer
in
the
parking
lots
by
changing
it
into
an
automated
process
by
pro
viding
f
ast
and
ef
ficient
parking
management.
The
automated
parking
management
system
made
up
of
2
stations.
One
is
at
entry
and
the
other
is
at
the
e
xit
at
the
parking
places.
These
stations
are
link
ed
to
main
processing
for
the
generation
of
parking
bills
depending
on
its
time.
3.
P
ARKING
MAN
A
GEMENT
SYSTEM
DESIGN
P
arking
management
system
architecture
i
s
sho
wn
in
Figure
1.
The
license
plate
recognition
system
consists
of
fi
v
e
phases:
Image
Acquisition
:
It
captures
the
image
and
forw
ards
the
image
to
the
ne
xt
phase
in
the
number
plate
recog-
nition
system.
Binarization
:
It
con
v
erts
the
image
into
gray-scale
image.
Noise
Remo
v
al
:
It
remo
v
es
the
noise
from
the
v
ehicle
number
plate.
Image
Pr
ocessing
–
Character
Se
gmentation:
It
e
xtracts
and
di
vides
all
the
characters
into
indi
vidual
images
from
the
license
plate
images.
–
Character
Recognition:
It
v
erifies
the
obtained
characters
with
the
database
Storing
into
Database:
It
stores
the
license
plate
number
with
input
time
into
the
database.
Bill
Generation:
It
generates
the
bill
amount
based
on
time
at
the
e
xit
station.
Figure
1.
P
arking
Management
System
Architecture
Indonesian
J
Elec
Eng
&
Comp
Sci
V
ol.
13,
No.
1,
January
2019:
77
–
84
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
79
4.
METHODS
USED
In
this
section
dif
ferent
algorithms
used
to
implement
number
plate
recognition
system
are
discussed.
4.1.
Edge
based
segmentation
Edge
based
se
gmentation,
it
is
the
position
of
pix
els
in
the
image
that
ha
v
e
the
close
similarity
to
the
bound-
aries
of
the
object
s
seen
i
n
the
ima
g
e
[13,
14].
It
is
then
assumed
that
since
it
is
a
boundary
of
a
re
gion
or
an
object,
it
is
closed
and
that
the
number
of
objects
of
interest
is
equal
to
the
number
of
boundaries
in
an
image.
F
or
correctness
of
the
se
gmentation,
the
perimeter
of
the
boundaries
detected
must
be
approximately
equal
to
that
of
the
object
in
the
input
image
[15].
F
or
instance,
the
methods
ha
ving
problems
with
images
that
are:
Edge-less
Additional
noise
Smooth
Boundary
T
e
xture
Boundary
and
so
on.
The
other
problems
of
these
techniques
are
emer
ge
from
the
f
ailure
to
adjust/
calibrate
gradient
function
accordingly
thus
produces
undesirable
results
as:
The
re
gion
which
is
se
gmented
might
be
smaller
or
greater
than
the
original.
-
The
edges
of
the
se
gmented
re
gion
might
not
be
connected
o
v
er
or
under
-se
gmentation
of
the
image
(arising
of
bogus
edges
or
missing
edges)
4.2.
Region
gr
o
wing
algorithm
This
algorithm
is
an
easy
re
gion-based
image
se
gmentation
procedure
which
is
further
also
cate
gorized
as
a
pix
el
–based
image
se
gmentation
procedure
therefore
it
in
v
olv
es
in
the
s
election
of
initial
seed
points.
This
approach
to
se
gmentation
inspects
the
neighboring
pix
el
of
that
initial
seed
points
and
it
will
decide
whether
the
neighbors
should
be
added
to
the
re
gion
or
not.
It
process
go
through
ag
ain,
in
the
same
manner
as
a
general
data
clustering
algorithms.
The
basic
disadv
antages
of
histogram-based
re
gion
observ
ation
is
that
the
histogram
pro
vides
no
spatial
information
(only
the
distrib
ution
of
gray
le
v
els).
It
utilize
the
foremost
certitudes
that
the
pix
els
which
are
closed
with
each
other
ha
v
e
similar
gray
v
alues
[16,
17].
This
re
gion
gro
wing
approach
is
quite
opposite
of
the
split
and
mer
ge
approaches
where
the
initial
set
of
small
re
gions
are
repeatedly
mer
ged
according
to
the
similarity
constraints.
It
starts
by
choosing
an
arbitrary
seed
pix
el
and
the
re
gion
is
gro
wn
by
adding
the
seed
pix
el
with
the
neighboring
pix
els
which
are
similar
to
each
other
,
and
increases
the
size
of
the
area
or
re
gion.
When
the
gro
wth
of
the
one
pix
el
is
stop
then
it
choose
another
seed
pix
el
which
is
not
yet
belongs
to
the
re
gion
which
is
already
used
and
then
start
the
same
process
ag
ain.
This
entire
process
is
continue
until
all
the
pix
els
belongs
to
some
of
the
re
gion.
Re
gion
gro
wing
methods
mostly
gi
v
es
good
se
gmentation
that
correlates
well
to
the
noted
edges
[18].
4.3.
Region-Based
Segmentation
The
main
objecti
v
e
of
se
gmentation
is
to
di
vide
an
image
into
re
gion
[19].
Some
of
the
se
gmentation
methods
such
as
thresholding,
the
objecti
v
e
of
this
method
is
achie
v
ed
by
looking
the
boundaries
of
the
re
gion
based
on
the
interrupt
in
the
gray
le
v
el
or
color
properties.
Re
gion
based
se
gmentation
ha
ving
the
ability
to
determine
the
re
gion
directly
[20].
4.4.
Character
Recognition
Lo
w
resolution
template
matching
method
is
acquired,
mainly
by
using
the
lo
w
pix
el
resolution
to
represent
an
image
and
tem
plate
that
to
be
recognized
[21].
Each
matrix
elements
that
correlates
to
a
sub-matrix
in
high
resolution
matrix.
The
element’
s
v
alue
is
the
a
v
erage
of
the
pix
el
gr
ay
v
alues
that
correlates
in
high
resolution
sub-
matrix.
Comparing
wi
th
the
high
resolution
matching
algorithms,
the
true
identification
rate
of
the
each
character
and
numbers
is
considerably
increased.
The
cause
is
that
if
the
resolution
goes
through
a
moderate
reduction,
then
the
error
produced
by
the
image
distortion
and
noise
will
be
reduced.
The
recognition
error
of
the
letters
and
the
numbers
mostly
occurs
in
fe
w
characters
with
v
ery
similar
main
structures
b
ut
some
detailed
dif
ferences
such
as
B
and
8,
O
and
0,
S
and
5
[22].
APMS
for
IVNP
,
(Asha
singh)
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80
ISSN:
2502-4752
4.5.
Corr
elation
The
measure
of
de
gree
to
which
tw
o
v
ariables
are
agreed,
not
require
in
ac
tual
v
alues
b
ut
in
general
beha
v-
ior
[23,
24].
The
tw
o
v
ariables
are
the
corresponding
to
the
pix
el
v
alues
in
the
tw
o
images,
templates
and
origin.
I
;J
=
E
[(
I
I
)(
J
J
)]
I
J
(1)
where
I,
J
are
the
v
ariables
of
the
corresponding
pix
el
v
alues
of
images,
E
,
&
are
co
v
ariance,
mean
and
v
ariances.
4.6.
T
emplate
Matching
It
is
a
technique
used
to
classify
objects.
T
emplate
is
a
small
image
or
a
sub
image.
The
main
objecti
v
e
is
to
find
phenomenon
of
this
template
in
a
lar
ger
image
that
is
to
find
the
matches
of
this
template
in
the
image.
T
emplate
matching
approaches
compare
the
part
of
the
images
ag
ainst
one
another
[25,
26].
Sample
image
is
used
to
identify
similar
objects
in
the
origin
image.
It
has
been
a
classi
cal
approach
to
the
complications
of
locating
and
identifying
an
object
in
an
image.
This
techniques
especially
in2-D
cases
has
man
y
applications
in
object
tracking,
compression
of
an
image,stereo
correspondence
and
other
computer
vision
applications.
There
are
se
v
eral
matching
methods
b
ut
normalized
cross
correlation
and
the
square
root
of
sum
of
square
dif
ference
are
used
as
the
measure
for
similarity
.
Moreo
v
er
,
man
y
other
techniques
to
match
the
templates,
such
as
sum
of
the
Absolute
Dif
ferences
and
sequential
similarity
detection
algorithm
are
acquired
in
man
y
applications
for
pattern
recognition,
video
compression,
etc.,
Additionally
,
this
template
matching
method
has
been
v
astly
used
in
v
arious
applications,
for
e
xample,
e
xtraction
of
container
identity
code
image
se
gmentation,
etc.,
The
correlating
pix
el
v
alues
in
the
template
and
origin
images
are
compared
using
this
algorithm
to
identifying
the
characters
on
the
v
ehicle
license
plate
[27,
28].
Figure
2.
Proposed
P
arking
Management
System
Indonesian
J
Elec
Eng
&
Comp
Sci
V
ol.
13,
No.
1,
January
2019:
77
–
84
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
81
5.
PR
OPOSED
METHOD
In
this
section,
step
by
step
process
of
automated
parking
management
system
is
discussed
and
is
sho
wn
in
Figure
2.
Step
1:
Initially
,
the
v
ehicle
image
is
captured
and
is
considered
as
input
image.
Step
2:
The
input
color
image
is
con
v
erted
into
gray-scale
image
to
identify
i
mportant
features
of
the
image
(i.e.,edge
information)
and
also
shades
of
gray-scale
image
gradually
changes
from
byte
to
byte
.
Step
3:
Median
filter
is
applied
on
the
gray-scale
image
to
reduce
the
noise
lik
e
salt
and
pepper
from
the
images
where
it
reduces
noise
and
preserv
e
edges.
Step
4:
Morphological
image
processing
is
done
on
the
median
filtered
image
since
the
image
may
contain
numerous
imperfection.
Dilation
and
eroding
operations
are
applied
using
structural
element.
F
or
probing
and
e
xpanding
the
characters
in
the
image
dilation
is
used
and
for
shrinking
eroding
is
used.
Morphological
image
is
generated
by
sub-
tracting
the
eroding
image
from
dilation
image
for
edge
enhancement.
Step
5:
Edge
brightening
is
done
on
the
morphological
image
for
easy
e
xtraction
of
the
characters
and
is
con
v
erted
into
binary
image.
Step
6:
Further
,
thinning
is
applied
on
the
binary
image
to
fill
the
entire
characters
in
the
license
plate.
Step
7:
Selection
of
a
re
gion:
It
remo
v
es
all
the
small
objects
from
the
binary
image
and
selects
the
particular
re
gion
in
the
license
plate
i.e.,
Characters
with
in
the
license
plate.
Step
8:
Finally
at
the
entry
station,
the
e
xtracted
characters
i.e.,
license
plate
number
and
entry
time
are
stored
in
the
entry
le
v
el
number
plate
database.
Step
9:
At
the
e
xit
station,
the
steps:1-8
are
repeated,
the
e
xtracting
license
plate
number
and
e
xit
time
are
stored
in
the
e
xit
le
v
el
number
plate
database.
Step
10:
Using
template
matching
algorithm,
the
characters
of
the
entry
and
e
xit
number
plates
are
compared
wi
th
the
help
of
correlation.
Step
11:
After
matching,
the
parking
bill
is
generated
based
on
the
entry
and
e
xit
time
based
on
parking
bill
rates.
P
arking
Bill
Rates
40
rupees
for
first
one
hours
of
parking
Extra
20
Rupees
for
each
additional
hour
Extra
50
rupees
after
six
hours
Selection
of
the
v
ehicle
be
yond
a
minute
is
char
ged
as
an
hour
1000
Rupees
for
each
24
hours
6.
EXPERIMENT
AL
RESUL
TS
In
this
simulation,
P
arking
bill
is
calculated
for
50
images
of
license
plates.
The
output
is
sho
wn
for
4
license
plate
images
as
sho
wn
in
Figure
3.
The
preprocessed
number
plate
is
gi
v
en
as
input
and
after
processing
the
output
is
gi
v
en
and
stored
in
the
database.
The
parking
bill
is
calculat
ed
based
on
the
entry
and
e
xit
time.
T
able.
2
pro
vides
the
ef
ficienc
y
of
the
proposed
parking
management
system.
On
an
a
v
erage
of
95
:
23%
is
achie
v
ed
in
recognition
of
license
plates
for
the
proposed
system.
APMS
for
IVNP
,
(Asha
singh)
Evaluation Warning : The document was created with Spire.PDF for Python.
82
ISSN:
2502-4752
T
able
1.
P
arking
Bill
Rates
for
1
Day
HOUR
PRICE
in
Rs
HOUR
PRICE
in
Rs
HOUR
PRICE
in
Rs
1
40
9
290
17
690
2
60
10
340
18
740
3
80
11
390
19
790
4
100
12
440
20
840
5
120
13
490
21
890
6
140
14
540
22
940
7
190
15
590
23
990
8
240
16
640
24
(1
D
A
Y)
1000
Figure
3.
Result
T
able
2.
Accurac
y
Rate
Detection
License
Plate
Recognition
Generation
of
P
arking
Bill
Correct
87%
100%
Error
Rate
13%
100%
A
v
erage
95.23%
100%
Indonesian
J
Elec
Eng
&
Comp
Sci
V
ol.
13,
No.
1,
January
2019:
77
–
84
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
83
7.
CONCLUSION
AND
FUTURE
SCOPE
In
t
his
system,
v
ehicle
license
plates
are
designed
as
the
crucial
task
for
parking
management
system.
It
performs
a
crucial
task
in
future
traf
fic
control
and
parking
system.
This
system
studies
the
license
plate
recognition
of
the
v
ehicles
based
on
neutral
netw
orks.
The
recognition
task
is
performed
on
50
license
plate
images
of
the
v
ehicles,
out
of
44
are
matched
successfully
on
an
a
v
erage
87
percent
which
is
a
great
success
rate,
thereby
fulfilling
the
principles
of
the
about
tasks.
K
e
y
element
of
the
system
are
successfully
designed
and
implemented.
The
proposed
system
recognizes
the
license
plate
and
generates
the
parking
bills
along
with
its
entry-time
and
e
xit-time
of
the
v
ehicles.
License
plates
recognition
system
has
man
y
applications.
These
can
be
used
at
the
parking
lots
where
the
parking
of
the
v
ehicle
is
done
without
w
asting
time,
and
there
is
no
need
for
in
v
olv
ement
of
man
po
wer
.
This
system
can
also
be
used
at
the
toll
g
ates
in
the
highw
ays
and
also
us
ed
for
identifying
the
v
ehicles
which
is
are
not
follo
wing
the
traf
fic
rules,
also
in
finding
the
theft
v
ehicles
by
maintaining
this
systems
on
the
highw
ays
for
l
ocating
the
v
ehicles.
Using
this
manual
w
ork
can
be
reduced
thus
it
impro
v
es
the
ef
ficienc
y
of
the
parking
system.
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