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


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


Introduction7


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

Binarization10

Image linearization (thresholding) refers to the

conversion of a gray-scale image into a binary image.


Binarization of Input Image

Salt-Pepper Noise removal11

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

Segmentation12

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 Analysis13

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.


14Example using CCA


Word Segmentation using CCA

15Example using CCA

Main part :

Sup script :

Sub script :

Ccdown :


Feature extraction16

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 itsmaximum radius.

Example of GFD18


The similarity between two shapes are measured

by the city block distance between the two set of

GFDs.

រ្�khmer OS UI font and its GFD

Recognition Process19


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 : Score20


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 Transform23

It is the transforming into polar image f(r, θ), for an

input image f(x, y).

c

ccc xx

yyyyxxr

arctan,)()( 22

1

0

1

0

1and

1where

M

yc

N

xc y

Nyx

Mx

shapetheof(barycent)centroidofrcoordinatotheis),( cc yx


Polar Raster Grid24

Polar raster sampling

Polar image Polar raster sample image in Cartesian space


Polar Raster Grid25

Binary polar raster sample shape images


Shape, shape normalization and its polar transform

2D- Fourier Transform26

2-D Fourier transform on polar raster sample

image f(r, ):

where 0r<R and i = i(2/T) (0 i<T); 0<R,

0<T. R and T are the radial frequency resolution

and angular frequency resolution respectively.

r i

i T

i

R

rjrfPF )]

2(2exp[),(),(


Normalization27

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

PFGFD


Future Work28

Integrate GFD into the system Exploit the possibility to improve the accuracy by

using Classifier for Training module



Thank for your attention !!!

Reference30


[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 InformationTechnology. 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.

Reference31


[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 USADepartment of Statistics, Stanford University, Stanford 94305-9025 CA. USA.

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Khmer ocr using gfd