Ngày tải lên :
24/04/2014, 13:02
... N,
N
>
0,
p
E(0,1)
.
N:
the
number
of
states
in
amodel;
The
functions
Fq
and
symbol
are
given
by
(5)
M
the
total
number
of
observation
symbols;
The
functions
andsym
lak
A
(a,
)NN
:
the
state
transition
probability
matrix.
and
(6)
respectively.
and
(6)
respectively.
~~B=(b
)Am
:
the
observation
symbol
probability
For
Krawtchouk
polynomials,
the
weight
B
m
(ti
sl
function
c(x)
and
squared
norm
p(n)
are
given
by
matrix
in
each
state ...
;T
=
(;Ti
)N
the
initial
state
probability
matrix.
NNx
c(x;
p,
N)
=
K
x
(1
-
p)Nx-
(14)
3.2
System
Overview
n
n
p(n;
p,
N)
=
(_P)n
rl
,
0
<
n
<
N
-1
.
(15)
At
the
top
level,
a
traditional
DHMMs-based
character
(-N)n
K
P
)
recognition
system
can
be
divided
into
two
basic
functional
components:
training
and
recognition.
Both
Same
to
the
Tchebichef
polynomials,
instability
also
training
and
recognition
share
a
common
pre-processing,
exists
among
Krawtchouk
polynomials.
The
definition
of
frames
generation
and
feature
extraction
stage.
weighted
Krawtchouk
polynomial
{Kn(x;p,N)}
is
In
the
training
phase,
after
applying
pre-processing
steps
including
binarization,
noise
removal,
boundary
Kn
(x;
~,
N)
x;
p,
N)
(16)
obtainment,
and
size
normalization,
an
image
p(n;
p,
N)
I(x,
y)
(0
<
x,
y
<
L)
is
segmented
into
T
frames
The
Krawtchouk
moments
of
order
(n
±
m)
in
term
of
frame(i)
(1
<
i
<
T)
using
sliding
window
technique
to
suit
for
DHMMs
recognition
engine.
The
feature
weighted
Krawtchouk
polynomials
for
an
image
with
extraction
module
then
transforms
a
frame
intensity
function
I(x,
y)
and
N
x
M
pixels
are
defined
as
image
frame(i)
into
a
feature
vector
fv(i),
which
is
then
Krn
m
=E
Kn(x;
p1
,N
-
)Krn
(y;
p2
,M
-
)I(x,y)
(17)
translated
into
a
symbol
O(i)(l
.<i<.T)
by
clustering
x=O
y=o
algorithm
(VQ).
The
codebook
with
M
code
words
output
by
VQ
is
kept
for
further
use
to
quantized
feature
790
COMBINING
DISCRETE
ORTHOGONAL
MOMENTS
AND
DHMMS
FOR
OFF-LINE
HANDWRITTEN
CHINESE
CHARACTER
RECOGNITION
Xianmei
Wang,
Yang
Yang,
and
Kang
Huang
School
of
Information
and
Engineering
University
of
Science
and
Technology
Beijing
No.
30,
Xueyuan
Road,
Beijing,
China,
100083
plum-wanggtom.com
Abstract
moments
requires
a
coordinate
transformation
which
may
Discrete
orthogonal
moment
set
is
one
of
the
novel
also
cause
precision
loss.
feature
moment-based
descriptors
for
image
analysis.
Some
researchers
such
as
MuKundan
et
al.
have
The
Tchebichef
moments
and
Krawtchouk
moments
are
suggested
the
use
of
discrete
orthogonal
moments
to
the
two
representatives
in
this
class.
This
paper
studies
overcome
the
problems
associated
with
the
continuous
the
performance
of
the
two
discrete
orthogonal
moments
orthogonal
moments.
In
the
past
few
years,
some
in
the
recognition
of
off-line
handwritten
Chinese
amount
different
discrete
orthogonal
moments
are
proposed
such
in
words
under
Discrete- time
Hidden
Markov
Models
as
Tchebichef
moments
[5-7]
and
Krawtchouk
moments
(DHMMs)
framework.
The
lower
order
moments
are
[8]
[9].
These
discrete
orthogonal
moments
are
directly
employed
asfeatures.
A
serial
of
experiments
are
carried
defined
in
the
image
coordinate
space
[(O,
0),
(N-1,
M-
out
to
compare
their
performance
with
that
of
the
1)].
The
implementation
of
the
discrete
orthogonal
continuous
orthogonal
movements
such
as
Zernike
and
moments
doesn't
involve
any
numerical
approximation
Legendre.
Experimental
results
suggest
that
the
and
coordinate
transformation.
These
properties
make
recognition
performance
of
two
discrete
orthogonal
discrete
orthogonal
moments
much
more
suitable
for
2-D
moments
is
higher
than
that
of
the
continuous
discrete
images
as
pattern
features.
It
has
been
shown
that
these
moments.
In
additional,
different
values
of
the
number
of
discrete
orthogonal
moments
have
better
performance
zones,
observation
symbols
and
states
are
also
used
to
than
the
conventional
continuous
orthogonal
moments
find
the
better
model
structure
for
the
new
approach.
for
image
reconstruction
[5]
[8]
[9].
As
we
know,
for
off-line
handwritten
character
Keywords:
Discrete
orthogonal
moments;
DHMMs;
Off
recognition,
most
of
obstacles
remain
in
the
strong
line
handwritten
character
recognition.
variability
of
the
handwriting
styles.
Hidden
Markov
models
(HMMs)
are
stochastic
models
which
can
deal
with
dynamic
properties
and
variations
among
human
1.
INTRODUCTION
handwriting.
Since
the
last
decade,
HMMs
have
been
widely
used
for
off-line
handwritten
character
Moments
with
orthogonal
basis
functions,
introduced
recognition.
There
are
basically
two
classes
of
HMMs
by
Teague
[1],
have
minimal
information
redundancy
in
depending
of
the
type
of
observation
sequence,
i.e.
a
moment
set.
In
this
class,
the
two
most
important
discrete- time
HMMs
(DHMMs)
and
continuous-time
orthogonal
moments
which
have
been
extensively
HMMs
(CHMMs).
Both
of
them
have
been
successfully
researched
in
pattern
recognition
field
are
Zernike
applied
to
the
recognition
of
off-line
handwritten
moments
and
Legendre
moments
[2-4].
Now
they
have
character.
However,
DHMMs
are
more
attractive
because
been
widely
used
as
fundamental
features
for
character
of
their
low
computational
cost.
recognition.
But
both
Zemike
and
Legendre
moments
In
this
paper,
we
proposed
a
method
by
extracting
belong
to
the
class
of
continuous
moments.
For
digital
discrete
orthogonal
moments
feature
for
DHMMs-based
images,
their
computation
requires
numerical
character
recognition.
Then
we
study
and
compare
the
approximation
of
continuous
integral.
This
process
can
discrete
orthogonal
moments
for
unconstrained
off-line
cause
error,
especially
when
the
order
of
the
moments
handwritten
Chinese
amount
in
words
recognition
with
increases.
Additionally,
the
use
of
Zernike
and
Legendre
12
Chinese
words.
In
the
literature
of
image
processing,
'
~~~~~~~~~it
is
well
established
that
the
important
and
perceptually
Proc.
5th
IEEE
Int.
Conf.
on
Cognitive
Informatics
(ICCI'06)
Y.Y.
Yao,
Z.Z.
Shi,
Y.
Wang,
and
W.
Kinsner
(Eds.)78
1
-4244-0475-4/06/$20.OO
@2006
IEEE78
... no
matter
the
number
Our
work
detailed
in
this
paper
deals
with
the
of
states
is
set
to
6,
9
or
12.
It
also
can
be
seen
that
the
recognition
of
the
isolated
unconstrained
off-line
highest
recognition
accuracy
can
be
achieved
by
setting
handwritten
Chinese
amount
in
words
including
Chinese
N
to
9.
However,
the
differences
in
accuracy
rate
for
the
characters
from
V
to
X
and
JU.
All
the
handwritten
values
of
N
with
9
and
12
are
small.
Additionally,
Table
character
samples
used
in
the
following
experiments
1
also
indicates
that
the
recognition
accuracy
of
Legendre
were
collected
by
our
laboratory.
They
are
written
by
moments
is
the
lowest.
numerous
writers
and
in
various
writing
styles.
There
are
totally
11,966
binary
digital
images.
We
used
8,366
Table
2.
Recognition
speed
of
using
different
images
for
training
and
3,600
images
for
testing.
orthogonal
moments
with
M=64,
Z=4
All
experiments
were
performed
on
a
COMPAQ
Evo
tate
Number
N610C
notebook
PC
with
768M
memory
and
1.8GHz
M
oen
6
9
12
CPU.
All
programs
are
written
with
Matlab
6.0
language.
E
Because
of
its
simplest
form,
(11)
was
selected
for
the
Tchebichef
513
477
442
function
8'(n,
N)
to
maintain
the
equal
weight
of
Krawtchouk
515
478
450
different
Tchebichef
moments.
In
(17),
the
condition
of
Zernike
239...