Khmer ocr using gfd

Mr. Sovann EN
5th
Year Engineering student
Dept. Computer Science & Communication
Institute of Technology of Cambodia
Phnom Penh, Cambodia
Khmer OCR Using Generic Fourier
Descriptor

Content
Khmer OCR Using Generic Fourier Descriptor
2
 Introduction
 Khmer OCR System
 Pre-processing
 Segmentation
 Feature Extraction
 Recognition Process
 Generic Fourier Descriptor In detail
 Future work

Introduction
 Optical Character Recognition (OCR) is the
electronic conversion of scanned images into machine-
encoded text.
 OCR makes it possible to edit the text, to search for
a word or a phrase, to display or to print a copy free of
scanning artifacts and so on.
3
Khmer OCR Using Generic Fourier Descriptor Back

Introduction
 The ideas started prior to World War II.
Throughout the years, many reliable commercial and
academic prototypes have been developed in many
natural languages.
 Still, due to the lacked effort in Khmer OCR, there
is no reliable Khmer OCR software.
4

Introduction
natural languages.
5

Introduction
natural languages.
6

Introduction
7
 The current system is based on Mr. Vanna Kruy’s
work for his master degree at Waseda University.
 The Objective is to produce a reliable Khmer OCR
system which is independent of Font and Size.

Khmer OCR System
 OCR System consist of four majors stages :
8
 Pre-processing
 Segmentation
 Feature Extraction
 Post processing

Pre-processing
9
 Pre-processing aims to produce data that are easy
for the OCR systems to operate accurately.
 The main objectives of pre-processing are :
 Binarization
 Noise removal

Binarization
10
 Image linearization (thresholding) refers to the
conversion of a gray-scale image into a binary image.
Binarization of Input Image

Salt-Pepper Noise removal
11
Salt-and-pepper noise is a kind of noise which is
usually caused by small unnecessary dots produced by
either the scanner or the source document itself.
Particle removal

Segmentation
12
 Segmentation aims to produce each component to
be recognized by the system.
 The process is to separate the text of a page into
each separate line, then to separate each line into
Vertical Component, and finally produce each
independent symbol.

Connected Component Analysis
13
 Connected Component is a group of pixels
accumulating together to form a shape.
 The process is to separate the text of a page into
each separate line, then to separate each line into
Vertical Component, and finally produce each
independent symbol.

14
Example using CCA
Word Segmentation using CCA

15
Example using CCA
Main part :
Sup script :
Sub script :
Ccdown :

Feature extraction
16
 In feature extraction stage, each character is
represented as a feature vector which becomes its
identity.
The major goal of feature extraction is to extract a
set of features which maximizes the recognition rate
with the least amount of elements.

Generic Fourier Descriptor
17
 GFD is derived by applying two-dimensional
Fourier transform on a polar-raster sampled shape
image.
 Polar-raster sampled : the image obtained after
circularly sampling an object in an image up to its
maximum radius.

Example of GFD
18
 The similarity between two shapes are measured
by the city block distance between the two set of
GFDs.
ររkhmer OS UI font and its GFD

Recognition Process
19
 To recognize, we compare the GFD’s score
 The score is obtain by comparing the CCs features
vector with CCs feature in database.

Recognition Process : Score
20
 The similarity between two shapes is measured by
the City-Block distance of the two feature vectors of
the shape.
 The lower value means the more similar the shapes
are.

Generic Fourier Descriptor In detail
21
 GFD is derived by applying two-dimensional
Fourier transform on a polar-raster sampled shape
image.
 A region-base shape descriptor proposed by
Dengsheng Zhang & Guojun Lu. It is confirm to
outperforms common contour-based and region-based
shape descriptors.

Algorithm to computer GFD
22
 Input the binary shape image data f(x, y)
 Get centroid of the shape (xc, yc)
 Set the centroid as the origin
 Compute Polar Fourier Transform
 Calculate Fourier Descriptor
 Output GFD

Polar Transform
23
 It is the transforming into polar image f(r, θ), for an
input image f(x, y).
c
c
cc
xx
yy
yyxxr
−
−
=−+−= arctan,)()( 22
θ
∑∑
−
=
−
=
==
1
0
1
0
1
and
1
where
M
y
c
N
x
c y
N
yx
M
x
shapetheof(barycent)centroidofrcoordinatotheis),( cc yx

Polar Raster Grid
24
 Polar raster sampling
Polar image Polar raster sample image in Cartesian space

Polar Raster Grid
25
 Binary polar raster sample shape images
Shape, shape normalization and its polar transform

2D- Fourier Transform
26
 2-D Fourier transform on polar raster sample
image f(r, θ ):
where 0≤r<R and θi
= i(2π/T) (0≤ i<T); 0≤ρ<R,
0≤φ<T. R and T are the radial frequency resolution and
angular frequency resolution respectively.
∑∑ +−=
r i
i
T
i
R
r
jrfPF )]
2
(2exp[),(),( φ
π
ρπθφρ

Normalization
27
 Translation invariant due to using centroid as
origin.
 Scale normalization is archived due to the
proportional equality:
}
|)0,0(|
|),(|
,...,
|)0,0(|
|)0,(|
,...,
|)0,0(|
|),0(|
,...,
|)0,0(|
|)1,0(|
,
|)0,0(|
{
PF
nmPF
PF
mPF
PF
nPF
PF
PF
area
PF
GFD =

Future Work
28
 Integrate GFD into the system
 Exploit the possibility to improve the accuracy by
using Classifier for Training module

Thank for your attention !!!

Reference
30
[1] V. Kruy. Preliminary Experiment on Khmer OCR. Kameyama Laboratory,
Waseda Univerisy, Japan.
[2] Thesis for master degree, Khmer OCR, Vanna Kruy.
[3] D. Zhang and G. Lu. Shape-based image retrieval using generic Fourier descriptor.
Gippsland School of Computing and Information
Technology. Monash University. Churchill, Victoria 3842, Australia.
[4] Thesis for Doctoral Degree, chapter 6: Generic Fourier Descriptor, Dengsheng
Zhang.
[5] J.C.Rupe. Vision-Based Hand Shape Identification for Sign Language
Recognition. Department of Computer Engineering Kate Gleason College of
Engineering Rochester Institute of Technology Rochester, NY.

Reference
31
[6] D. Dimov. A polar-Fourier-Wavelet’s Transform for Effective CBIR. 3rd
ADBIS
workshop on Data mining & Knowledge Discovery
[7] I. Lengieng, K. Sochenda and C. Sokhour. , Khmer OCR for Limon R1 Size 22
Report, PAN Localization Cambodia (PLC) of IDRC.er OCR
[8] A. Averbuch, R.R. Coifmany , D.L. Donohoz M. Eladx M. Israeli. Fast and
Accurate Polar Fourier Transform.
Department of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel.
Department of mathematics, Yale University, New Haven CT 06520-8283 USA
Department of Statistics, Stanford University, Stanford 94305-9025 CA. USA.

Khmer ocr using gfd

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