Application of Genetic Algorithm in Optical Character...

54 Farhan Inamdar, Pankaj Dadhich, Yogen Lohite, Yogesh Chandrate

International Journal of Innovations & Advancement in Computer Science

IJIACS

ISSN 2347 – 8616

Volume 6, Issue 2

February 2017

Application of Genetic Algorithm in Optical Character

Recognition Techniques

Farhan Inamdar1, Pankaj Dadhich

2, Yogen Lohite

3, Yogesh Chandrate

4,

1,2,3,4Lecturer Guru Gobind Singh Polytechnic, Nashik

Abstract— Optical Character recognition is major

technique in Image Processing fields which has various

application in obtaining more clear character in the field

of Medical, Industrial processes as well as Domestic. In

this technology scanned, Handwritten, printed or

typewritten text is converted into editable format to

obtain its more clear digital version.Charector

recognition may be carried out by online and offline in

online recognition stylus or electronic tablet is interface

with computer to abstract information about character

whereas in off-line recognition target is digitally scanned

by optical scanner.

In this paper I have concentrated on offline character

recognition along with genetic algorithm and its

advantages

Keywords- Genetic Algorithm ,Digitization Optical

Character Recognition(OCR),Pre-processing

1. INTRODUNCTION

Optical Character recognition is major technique in

Image Processing fields which has various

application in obtaining more clear character in the

field of Medical, Industrial processes as well as

Domestic. In this technology scanned, Handwritten,

printed or typewritten text is converted into editable

format to obtain its more clear digital

version.Charector recognition may be carried out by

online and offline in online recognition stylus or

electronic tablet is interface with computer to

abstract information about character whereas in off-

line recognition target is digitally scanned by

optical scanner. OCR consists of many phases such

as Pre-processing, Segmentation, Feature Extraction,

Classifications and Recognition. The input of one

step is the output of next step. The task of pre-

processing relates to the removal of noise and

variation in handwritten. Several area where OCR

used including mail sorting, bank processing,

document reading and postal address recognition

require offline handwriting recognition systems,

pattern recognition

Phases of General Character Recognition System

A) Pre-processing: In The pre-processing

phase, there is a series of operations performed on

the scanned input image such as noise removing and

normalisation. It enhances the image rendering it

suitable for segmentation the gray-level character

image is normalized into a window sized. After

noise reduction, we produced a bitmap image . Then,

the bitmap image was transformed into a thinned

image.

B) Segmentation: The Segmentation phase is

the most important process. Segmentation is done

by separation from the individual characters of an

image. Segmentation is done to make the separation

between the individual characters of an image.

Sometimes components of two adjacent characters

may be touched or overlapped and this situation

create difficulties in the segmentation task.



IJIACS

ISSN 2347 – 8616

Volume 6, Issue 2

February 2017

Touching or overlapping problem occurs frequently

because of modified characters in upper-zone and

lower-zone.

C) Feature Extraction: In this phase, features

of individual character are extracted. The

performance of an each character recognition

system that depends on the features that are

extracted. The extracted features from input

character should allow classification of a character

in a unique way. We used diagonal features,

intersection and open end points features, transition

features, zoning features, directional features,

parabola curve fitting–based features, and power

curve fitting–based features in order to find the

feature set for a given character.

D) Classification : The classification is the

process of identifying each character and assigning

to it the correct character class, so that texts in

images are converted in to computer understandable

form. This process used extracted feature of text

image for classification i.e. input to this stage is

output of the feature extraction process. Classifiers

compare the input feature with stored pattern and

find out best matching class for input. There are

many technique used for classification such as

Artificial Neural Network (ANN), Template

Matching, Support Vector Matching (SVM) etc.

E) Post-processing Module : The output of

Text Recognition Module is in the form text data

which is understand by computer, So there need to

store it in to some proper format( i.e. txt or MS-

Word )for farther use such as Editing or Searching

in that data.

2. LITERATURE REVIEW

Claudiu et al. (2011) [1] has investigated using

simple training data pre-processing gave us experts

with errors less correlated than those of different

nets trained on the same or bootstrapped data.

Hence committees that simply average the expert

outputs considerably improve recognition rates.

Georgios et al. (2010) [2] has presented a

methodology for off-line handwritten character

recognition. The proposed methodology relies on a

new feature extraction technique based on recursive

subdivisions of the character image. Feature

extraction is followed by a two-stage classification

scheme based on the level of granularity of the

feature extraction method. Classes with high values

in the confusion matrix are merged at a certain level

and for each group of merged classes, granularity

features from the level that best distinguishes them

are employed. Two handwritten character databases

(CEDAR and CIL) as well as two handwritten digit

databases (MNIST and CEDAR) were used in order

to demonstrate the effectiveness of the proposed

technique.

Sankaran et al. (2012) [3] has presented present a

novel recognition approach that results in a 15%

decrease in word error rate on heavily degraded

Indian language document images. Classical OCR

approaches perform poorly over complex scripts

such as those for Indian languages. Sankaran et al.

(2012) [3] addressed these issues by proposing to

recognize character n-gram images, which are

basically groupings of consecutive

character/component segments. Their approach was

unique, since they use the character ngrams as a

primitive for recognition rather than for

postprocessing. By exploiting the additional context

present in the character n-gram images, we enable

better disambiguation S between confusing

characters in the recognition phase. The labels

obtained from recognizing the constituent n-grams

are then fused to obtain a label for the word that

emitted them. Their method is inherently robust to

degradations such as cuts and merges which are

common in digital libraries of scanned documents.

We also present a reliable and scalable scheme for

recognizing character n-gram images. Tests on

English and Malayalam document images show

considerable improvement in recognition in the case

of heavily degraded documents.

Jawahar et al. (2012) [4] has propose a recognition

scheme for the Indian script of Devanagari.

Recognition accuracy of Devanagari script is not

yet comparable to its Roman counterparts. This is

mainly due to the complexity of the script, writing

style etc. Our solution uses a Recurrent Neural

Network known as Bidirectional Long- Short Term

Memory (BLSTM). Our approach does not require

word to character segmentation, which is one of the

most common reason for high word error rate.

Jawahar et al. (2012) [4] has reported a reduction of

more than 20% in word error rate and over 9%

reduction in character error rate while comparing

with the best available OCR system.

Badawy, W. et al. (2012) [6] has discussed the

Automatic license plate recognition (ALPR) is the

extraction of vehicle license plate information from

an image or a sequence of images. The extracted



IJIACS

ISSN 2347 – 8616

Volume 6, Issue 2

February 2017

information can be used with or without a database

in many applications, such as electronic payment

systems (toll payment, parking fee payment), and

freeway and arterial monitoring systems for traffic

surveillance. The ALPR uses either a color, black

and white, or infrared camera to take images.

Ntirogiannis et al. (2013) [7] has studied that the

document image binarization is of great importance

in the document image analysis and recognition

pipeline since it affects further stages of the

recognition process. The evaluation of a

binarization method aids in studying its algorithmic

behaviour, as well as verifying its effectiveness, by

providing qualitative and quantitative indication of

its performance. This paper addresses a pixel-based

binarization evaluation methodology for historical

handwritten/machine-printed document images. In

the proposed evaluation scheme, the recall and

precision evaluation measures are properly

modified using a weighting scheme that diminishes

any potential evaluation bias.

Yang et al. (2012) [8] has proposed a novel adaptive

binarization method based on wavelet filter is

proposed in this paper, which shows comparable

performance to other similar methods and processes

faster, so that it is more suitable for real-time

processing and applicable for mobile devices. The

proposed method is evaluated on complex scene

images of ICDAR 2005 Robust Reading

Competition, and experimental results provide a

support for our work.

3. PROPOSED WORK

Algorithm

• Read the unknown image

• Process the image (conversion into gray scale

and then into bit string of 0,1)

• Recognition of image

• Initialise the generation

• Evaluate fitness function each individual

• Select best 2 chromosome for next generation

• Preform crossover to generate new chromosome

• Perform mutation

• Stop the algorithm when best fitness value is

found

Flowchart

In this study we have used MATLAB package for

the application of genetic algorithm to initialize

application values. Furthermore Roulette Wheel

Selection method used as parents to crossover that

we have applied to a set of binary numbers (0,1

encoding) representing capital English alphabets.

The alphabets ranges from A to Z represent in a

matrix of 8 * 6 array dimension (see figure) which

behaves as initiated value for comparing.



IJIACS

ISSN 2347 – 8616

Volume 6, Issue 2

February 2017

Each sample (character) considered a chromosome,

which has 48 genes presented as Table as below

Apply Generic Algorithm:

For each population two or more chromosomes are

selected to be parents to crossover. The problem we

have faced here is to know how to select these

chromosomes to create a new offspring. There are

many methods to overcome this problem and for

selecting the best chromosome like Roulette Wheel

Selection, Boltzmann Tournament Selection for

Genetic, Rank Selection and some others. The

chromosomes with higher fitness have a higher

possibility to be selected to produce offspring for

the next generation. After many generations of

evolution, the optimal solution of the problem is

hopefully to be found in the population [4] [5] [6].

The selected fitness function that we have utilized

in our research is as follows:

Where:

α : Desired gene from the Actual data;

β : Fault level of a chromosome computed by

genetic algorithm and n : the number of genes in

chromosome.

Using the above fitness function, we have trained

our system like if the fault level of a chromosome is

equal to the desired the fitness value of that

chromosome will be equal to 1 or if generation

number is more than thirty (30) the process of GAs

will be terminated.

The system structure can be described as below,

Process the character before recognition since

procedures takes the unknown character and then

return the unknown character without empty or

points in to array. We have used 30 generations, for

each generation 10 chromosomes and for each

chromosome has 48 genes. The genes of all

individuals consist of either 0 or 1(assuming a

binary encoding for simplicity).

The First Generation:

Firstly, we generate chromosomes by combining all

features extracted from unknown character in array.

First test for unclear A:

For example if we have the following image

Character of unclear A

Initial chromosome for primary offspring :

Choosing two chromosomes with a higher fitness

and applying crossover and mutation operation to

get a new chromosome, which is considered an

initial for a second offspring and so on (see bold

rows below) and because of the great fitness

function values of these selected chromosomes

which mean that character have enough recognition

from the first steps and hence the process stop.



IJIACS

ISSN 2347 – 8616

Volume 6, Issue 2

February 2017

Two chromosomes with the high fitness function

value are as follows

Now we will choose two chromosomes with a

higher fitness and applying crossover and mutation

operation to get new chromosomes, the two selected

chromosomes are as bellow.

Using fitness calculation function we have found

that a function values are greater than the previous

two (02) bold chromosomes hence a crossover for

these chromosomes are given as follows.

Moreover the all configured chromosomes and then

crossover process or mutation as follows:

After account of fitness value that genetic algorithm

is able to identify the character in the 30th

generation.

Above Table consists final chromosome, which

specify character A after testing with different only

in one gene ( in the 37th position) comparing with

all initial chromosome which specify normal (clear)

character A as target.

In the similar way, we can recognise all alphabet

and numbers as well.

5. RESULT

This section explains the GUI of the MATLAB

toolkit and OCR using Genetic algorithm. The

following are the sample empty GUI window of the

MATLAB toolkit.



IJIACS

ISSN 2347 – 8616

Volume 6, Issue 2

February 2017

The various components marked in the above image

are explained as the following:

(1) Steps involved in GA tool for OCR.

(2) Select parent image – The push button option to

select parent image for correlation. The input

images to be fused are loaded and displayed in

these image frames (First Image, Second

Image).

(3) Initial chromosome for primary offspring – The

push button option to display the Input

Character Images

(4) Start GA for Character A – The push button

interface to initiate the GA for alphabet A

(5) Comparative Analysis – The push button option

to display the comparative analysis of (different

pixel).

Optimization Using Genetic Algorithm:

Input image to be recognise

Output editable .txt file.

4. ADVANTAGES AND APPLICATIONS

Advantages:

It provides efficient techniques for optimization

and machine learning applications

This project gives an alternative to traditional

optimization technique by using directed

random searches to locate optimal solutions

The Recognition problem is solved by the GA,

which may yield a different solution for the

same word each time.

95-97% of characters recognition.



IJIACS

ISSN 2347 – 8616

Volume 6, Issue 2

February 2017

Applications:

Optical character recognition has been applied to a

number of applications. Some of them are Listed

below

Invoice Imaging

Legal Industry

Banking

Healthcare

Captcha

Institutional Repositories and Digital Libraries

Optical Music Recognition

Automatic Number Recognition

Handwriting Recognition

5. CONCLUSION

The genetic algorithms being trained using

standard templates of the capital alphabets and

then calculate a fitness function value.

Character recognition process produces

comparatively big values for specific character

but for rest characters fitness values found small

in less time due to obvious features

Training accomplish that genetic algorithm was

able to identify the character in the 30th

generation.

It‟s also true that the fitness function did not

produce one „1‟ value which mean 100%

recognition for character „A‟ but accomplish

with 97% in the 30th generation with fitness

function value equal 0.5

The system was then tested for most of 26

characters and it shows 95-97% of characters

recognition

REFERENCES

[1] Khaled M.G Noaman, Jamil Abdulhameed M. Saif,

Ibrahim A.A. Alqubati, Optical Character

Recognition Based on Genetic Algorithms, Journal

of Emerging Trends in Computing and Information

Sciences, Vol. 6, No. 4 April 2015

[2] Dan ClaudiuCires¸an and Ueli Meier and Luca

Maria Gambardella and JurgenSchmidhuber,

“Convolutional Neural Network Committees for

Handwritten Character Classification”, 2011

International Conference on Document Analysis and

Recognition, IEEE, 2011.

[3] GeorgiosVamvakas, Basilis Gatos, Stavros J.

Perantonis, “Handwritten character recognition

through two-stage foreground sub-sampling”

,Pattern Recognition, Volume 43, Issue 8, August

2010.

[4] Shrey Dutta, Naveen Sankaran, PramodSankar K.,

C.V. Jawahar, “Robust Recognition of Degraded

Documents Using Character N-Grams”, IEEE, 2012.

[5] Naveen Sankaran and C.V Jawahar, “Recognition of

Printed Devanagari Text Using BLSTM Neural

Network”, IEEE, 2012.

[6] Yong-Qin Zhang, Yu Ding, Jin-Sheng Xiao, Jiaying

Liu and Zongming Guo1, “Visibility enhancement

using an image filtering approach”, Zhang et al.

EURASIP Journal on Advances in Signal Processing

2012.

[7] Badawy, W. "Automatic License Plate Recognition

(ALPR): A State of the Art Review." (2012): 1-1.

[8] Ntirogiannis, Konstantinos, Basilis Gatos, and

IoannisPratikakis. "A Performance Evaluation

Methodology for Historical Document Image

Binarization." (2013): 1-1.

[9] Yang, Jufeng, Kai Wang, Jiaofeng Li, Jiao Jiao, and

Jing Xu. "A fast adaptive binarization method for

complex scene images." In Image Processing (ICIP),

2012 19th IEEE International Conference on, pp.

1889-1892. IEEE, 2012.

[10] Sumetphong, Chaivatna, and

SupachaiTangwongsan. "An Optimal Approach

towards Recognizing Broken Thai Characters in

OCR Systems." Digital Image Computing

Techniques and Applications (DICTA), 2012

International Conference on. IEEE, 2012.

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