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1. INTRODUCTION

In the running world, there is growing demand for the software systems to recognize characters in

computer world when information is scanned through paper documents. As we know that we have a

number of newspapers and books which are in printed format related to different subjects. In these days

there is a huge demand in “storing the information available in these paper documents in to a computer

storage disk and then later reusing this information by searching process, to avoid damages or losses”.

One simple way to store information in these paper documents in to computer system is to first scan the

documents and then store them as IMAGES. But to reuse this information it is very difficult to read the

individual contents and searching the contents form these documents line-by-line and word-by-word. The

reason for this difficulty is the font characteristics of the characters in paper documents are different to

font of the characters in computer system. As a result, computer is unable to recognize the characters

while reading them. This concept of storing the contents of paper documents in computer storage place

and then reading and searching the content is called DOCUMENT PROCESSING. Sometimes in this

document processing we need to process the information that is related to languages other than the

English in the world. For this document processing we need an application software system named

CHARACTER RECOGNITION SYSTEM. This process is also called CHARACTER RECOGNITION

AND CONVERSION (CRC).

Thus our need is to develop character recognition software system to perform Document Image

Analysis which transforms documents in paper format to electronic format. For this process there are

various techniques in the world. We’ve chosen Character Recognition and Conversion.

HISTORY OF OCR

Early optical character recognition may be traced to technologies involving telegraphy

and creating reading devices for the blind. In 1914, Emanuel Goldberg developed a machine that

read characters and converted them into standard telegraph code. Concurrently, Edmund Fournier

d'Albe developed the Optophone, a handheld scanner that when moved across a printed page,

produced tones that corresponded to specific letters or characters.

In the late 1920s and into the 1930s Emanuel Goldberg developed what he called a "Statistical

Machine" for searching microfilm archives using an optical code recognition system. In 1931 he

was granted USA Patent number 1,838,389 for the invention. The patent was acquired by IBM.

With the advent of smart-phones and smart-glasses, OCR can be used in internet connected

mobile device applications that extract text captured using the device's camera. These devices

that do not have OCR functionality built-in to the operating system will typically use an

OCR API to extract the text from the image file captured and provided by the device. The OCR

API returns the extracted text, along with information about the location of the detected text in

the original image back to the device app for further processing (such as text-to-speech) or

display.

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TYPES OF OCR

Optical character recognition (OCR) – targets typewritten text, one glyph or character at a time.

Optical word recognition – targets typewritten text, one word at a time (for languages that use a space as

a word divider). (Usually just called "OCR".)

Intelligent character recognition (ICR) – also targets handwritten printscript or cursive text one glyph or

character at a time, usually involving machine learning.

Intelligent word recognition (IWR) – also targets handwritten printscript or cursive text, one word at a

time. This is especially useful for languages where glyphs are not separated in cursive script.

OCR is generally an "offline" process, which analyses a static document. Handwriting movement analysis can

be used as input to handwriting recognition.[13] Instead of merely using the shapes of glyphs and words, this

technique is able to capture motions, such as the order in which segments are drawn, the direction, and the

pattern of putting the pen down and lifting it. This additional information can make the end-to-end process

more accurate. This technology is also known as "on-line character recognition", "dynamic character

recognition", "real-time character recognition", and "intelligent character recognition".

1.1 PURPOSE

The main purpose of Character Recognition and Conversion is to perform document

processing of electronic document formats converted from paper formats more effectively and

efficiently. This improves the accuracy of recognizing the characters during document

processing compared to various existing available character recognition methods. Here Character

Recognition technique derives the meaning of the characters, their font properties from their bit-

mapped images.

The primary objective is to speed up the process of character recognition in document

processing. As a result the system can process huge number of documents with-in less time

and hence saves the time.

It aims to recognize multiple heterogeneous characters that belong to different universal

languages with different font properties and alignments.

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1.2 PROJECT SCOPE

The scope of our project Character Recognition and Conversion on a grid infrastructure is to

provide an efficient and enhanced software tool for the users to perform Document Image

Analysis, document processing by reading and recognizing the characters in research, academic,

governmental and business organizations that are having large pool of documented, scanned

images. Irrespective of the size of documents and the type of characters in documents, the

product is recognizing them, searching them and processing them faster according to the needs

of the environment.

1.3 Technology Used

The exact mechanisms that allow humans to recognize objects are yet to be understood,

but the three basic principles are already well known by scientists – integrity, purposefulness and

adaptability (IPA). These principles allows to replicate natural or human-like recognition.

Let’s take a look on how OCR recognizes text. First, the program analyses the structure

of document image. It divides the page into elements such as blocks of texts, tables, images, etc.

The lines are divided into words and then - into characters. Once the characters have been

singled out, the program compares them with a set of pattern images. It advances numerous

hypotheses about what this character is. Basing on these hypotheses the program analyses

different variants of breaking of lines into words and words into characters. After processing

huge number of such probabilistic hypotheses, the program finally takes the decision, presenting

you the recognized text.

1.4 How to use?

Using OCR is easy: the process generally consists of three stages: Open (Scan) the

document, Recognize it and then Save in a convenient format (DOC, RTF, XLS, PDF, HTML,

TXT etc.) or export data directly to one of Office applications such as Microsoft Word, Excel or

Adobe Acrobat.

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2. FEASIBILITY STUDY

A feasibility study is a high-level capsule version of the entire System analysis and Design

Process. The study begins by classifying the problem definition. Feasibility is to determine if it’s worth

doing. Once an acceptance problem definition has been generated, the analyst develops a logical model of

the system. A search for alternatives is analyzed carefully. There are 3 parts in feasibility study.

2.1 TECHNICAL FEASIBILITY

Evaluating the technical feasibility is the trickiest part of a feasibility study. This is because, at

this point in time, not too many detailed design of the system, making it difficult to access issues like

performance, costs on (on account of the kind of technology to be deployed) etc. A number of issues have

to be considered while doing a technical analysis. Understand the different technologies involved in the

proposed system before commencing the project we have to be very clear about what are the technologies

that are to be required for the development of the new system. Find out whether the organization currently

possesses the required technologies. Is the required technology available with the organization?

2.2 OPERATIONAL FEASIBILITY

Proposed project is beneficial only if it can be turned into information systems that will meet

the organizations operating requirements. Simply stated, this test of feasibility asks if the system will

work when it is developed and installed. Are there major barriers to Implementation? Here are

questions that will help test the operational feasibility of a project:

Is there sufficient support for the project from management from users? If the current system is well

liked and used to the extent that persons will not be able to see reasons for change, there may be

resistance.

Are the current business methods acceptable to the user? If they are not, Users may welcome a

change that will bring about a more operational and useful systems.

Have the user been involved in the planning and development of the project?

Early involvement reduces the chances of resistance to the system and in general and increases the

likelihood of successful project.

Since the proposed system was to help reduce the hardships encountered. In the existing manual

system, the new system was considered to be operational feasible.

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2.3 ECONOMIC FEASIBILITY

Economic feasibility attempts to weigh the costs of developing and implementing a new system,

against the benefits that would accrue from having the new system in place. This feasibility study gives

the top management the economic justification for the new system. A simple economic analysis which

gives the actual comparison of costs and benefits are much more meaningful in this case. In addition, this

proves to be a useful point of reference to compare actual costs as the project progresses. There could be

various types of intangible benefits on account of automation. These could include increased customer

satisfaction, improvement in product quality better decision making timeliness of information, expediting

activities, improved accuracy of operations, better documentation and record keeping, faster retrieval of

information, better employee morale.

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3. SOFTWARE USED

The software used in Character Recognition and Conversion (CRC) is MATLAB.

MATLAB (Matrix Laboratory) is a high-level language and interactive environment that enables

to you to perform computationally intensive faster than with traditional programming languages such as

C, C++ and Fortran.

MATLAB consists of 4 components such as;

Workspace

Command Window

Command History

File Editor Window

Workspace displays all the defined variables.

Command Window is used to execute commands in the MATLAB environment.

Command History displays record of the commands used.

File Editor Window defines your function.

3.1 Operating System

Windows 7 and higher versions

3.2 Hardware Requirement Specification

Processor: Intel core 2 dual or higher

RAM: 2GB

Memory Required: 5GB or higher

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4. SOFTWARE DESIGN

4.1 DATA FLOW DIAGRAM

The DFD is also called as bubble chart. A data-flow diagram (DFD) is a graphical

representation of the "flow" of data through an information system. DFD’s can also be used for the

visualization of data processing.

Figure 4.1: Level 0 DFD

Figure 4.2: Level 1 DFD

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Figure 4.3: Level 2 DFD

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5. CODING

5.1. Code for Creating Template %Letter clc; close all; A=imread('letters_numbers\A.bmp');B=imread('letters_numbers\B.bmp'); C=imread('letters_numbers\C.bmp');D=imread('letters_numbers\D.bmp'); E=imread('letters_numbers\E.bmp');F=imread('letters_numbers\F.bmp'); G=imread('letters_numbers\G.bmp');H=imread('letters_numbers\H.bmp'); I=imread('letters_numbers\I.bmp');J=imread('letters_numbers\J.bmp'); K=imread('letters_numbers\K.bmp');L=imread('letters_numbers\L.bmp'); M=imread('letters_numbers\M.bmp');N=imread('letters_numbers\N.bmp'); O=imread('letters_numbers\O.bmp');P=imread('letters_numbers\P.bmp'); Q=imread('letters_numbers\Q.bmp');R=imread('letters_numbers\R.bmp'); S=imread('letters_numbers\S.bmp');T=imread('letters_numbers\T.bmp'); U=imread('letters_numbers\U.bmp');V=imread('letters_numbers\V.bmp'); W=imread('letters_numbers\W.bmp');X=imread('letters_numbers\X.bmp'); Y=imread('letters_numbers\Y.bmp');Z=imread('letters_numbers\Z.bmp'); %lower case letters a=imread('letters_numbers\a.png');b=imread('letters_numbers\b.png'); c=imread('letters_numbers\c.png');d=imread('letters_numbers\d.png'); e=imread('letters_numbers\e.png');f=imread('letters_numbers\f.png'); g=imread('letters_numbers\g.png');h=imread('letters_numbers\h.png'); i=imread('letters_numbers\i.png');j=imread('letters_numbers\j.png'); k=imread('letters_numbers\k.png');l=imread('letters_numbers\l.png'); m=imread('letters_numbers\m.png');n=imread('letters_numbers\n.png'); o=imread('letters_numbers\o.png');p=imread('letters_numbers\p.png'); q=imread('letters_numbers\q.png');r=imread('letters_numbers\r.png'); s=imread('letters_numbers\s.png');t=imread('letters_numbers\t.png'); u=imread('letters_numbers\u.png');v=imread('letters_numbers\v.png'); w=imread('letters_numbers\w.png');x=imread('letters_numbers\x.png'); y=imread('letters_numbers\y.png');z=imread('letters_numbers\z.png');

%Number one=imread('letters_numbers\1.bmp'); two=imread('letters_numbers\2.bmp'); three=imread('letters_numbers\3.bmp');four=imread('letters_numbers\4.bmp'); five=imread('letters_numbers\5.bmp'); six=imread('letters_numbers\6.bmp'); seven=imread('letters_numbers\7.bmp');eight=imread('letters_numbers\8.bmp'); nine=imread('letters_numbers\9.bmp'); zero=imread('letters_numbers\0.bmp'); %*-*-*-*-*-*-*-*-*-*-*- letter=[A B C D E F G H I J K L M... N O P Q R S T U V W X Y Z]; number=[one two three four five... six seven eight nine zero];

lowercase = [a b c d e f g h i j k ... l m n o p q r s t u v w x y z]; character=[letter number lowercase]; templates=mat2cell(character,42,[24 24 24 24 24 24 24 ...

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24 24 24 24 24 24 24 ... 24 24 24 24 24 24 24 ... 24 24 24 24 24 24 24 ... 24 24 24 24 24 24 24 24 ... 24 24 24 24 24 24 24 24 ... 24 24 24 24 24 24 24 24 ... 24 24 24 24 24 24 24 24 ... 24 24]); save ('templates','templates') clear all

5.2. Read Lines

%function lines% % function [fl re]=lines_crop(im_texto) % Divide text in lines % im_texto->input image; fl->first line; re->remain line % Example: %clc; %im_texto=imread('blackwhite.jpg'); %figure,imshow(im_texto) %title('after bwareaopen') %im_texto = bwareaopen(im_texto,60); %figure,imshow(im_texto); %title('after bwareaopen') %[fl re]=lines(im_texto); %subplot(3,1,1);imshow(im_texto);title('INPUT IMAGE') %subplot(3,1,2);imshow(fl);title('FIRST LINE') %subplot(3,1,3);imshow(re);title('REMAIN LINES') im_texto=clip(im_texto); num_filas=size(im_texto,1); for s=1:num_filas if sum(im_texto(s,:))==0 nm=im_texto(1:s-1, :); % First line matrix %pause(1); rm=im_texto(s:end, :);% Remain line matrix %pause(1); fl = clip(nm); pause(1); re=clip(rm); %*-*-*Uncomment lines below to see the result*-*-*-*- %subplot(2,1,1);imshow(fl); %subplot(2,1,2);imshow(re); break else fl=im_texto;%Only one line. re=[ ]; end end %subplot(3,1,1);imshow(im_texto);title('INPUT IMAGE') %subplot(3,1,2);imshow(fl);title('FIRST LINE') %subplot(3,1,3);imshow(re);title('REMAIN LINES')

function img_out=clip(img_in)

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[f c]=find(img_in); img_out=img_in(min(f):max(f),min(c):max(c));

5.3. Read Letters in a Line

%function lines% %function letter_in_a_line function [fl re space]=letter_crop(im_texto) % Divide letters in lines

im_texto=clip(im_texto); num_filas=size(im_texto,2);

%figure,imshow(im_texto); %title('line sent in the function letter'); for s=1:num_filas s; sum_col = sum(im_texto(:,s)); if sum_col==0 k = 'true'; nm=im_texto(:,1:s-1); % First letter matrix %figure,imshow(nm); %title('first letter in the function letter_in_a_line'); %pause(1); rm=im_texto(:,s:end);% Remaining line matrix %figure,imshow(rm); %title('remaining letters in the function letter_in_a_line'); %pause(1); fl = clip(nm); %pause(1); re=clip(rm); space = size(rm,2)-size(re,2); %*-*-*Uncomment lines below to see the result*-*-*-*- %subplot(2,1,1);imshow(fl); %subplot(2,1,2);imshow(re); break else fl=im_texto;%Only one line. re=[ ]; space = 0; end end

function img_out=clip(img_in) [f c]=find(img_in); img_out=img_in(min(f):max(f),min(c):max(c));

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5.4. Recognize letters

%function read_letter function letter=read_letter(imagn,num_letras) % Computes the correlation between template and input image % and its output is a string containing the letter. % Size of 'imagn' must be 42 x 24 pixels % Example: % imagn=imread('D.bmp'); % letter=read_letter(imagn) %load templates global templates comp=[ ];

for n=1:num_letras

sem=corr2(templates{1,n},imagn); comp=[comp sem];

%pause(1) end

vd=find(comp==max(comp)); %*-*-*-*-*-*-*-*-*-*-*-*-*- if vd==1 letter='A'; elseif vd==2 letter='B'; elseif vd==3 letter='C'; elseif vd==4 letter='D'; elseif vd==5 letter='E'; elseif vd==6 letter='F'; elseif vd==7 letter='G'; elseif vd==8 letter='H'; elseif vd==9 letter='I'; elseif vd==10 letter='J'; elseif vd==11 letter='K'; elseif vd==12 letter='L'; elseif vd==13 letter='M'; elseif vd==14 letter='N'; elseif vd==15 letter='O'; elseif vd==16

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letter='P'; elseif vd==17 letter='Q'; elseif vd==18 letter='R'; elseif vd==19 letter='S'; elseif vd==20 letter='T'; elseif vd==21 letter='U'; elseif vd==22 letter='V'; elseif vd==23 letter='W'; elseif vd==24 letter='X'; elseif vd==25 letter='Y'; elseif vd==26 letter='Z'; %*-*-*-*-* elseif vd==27 letter='1'; elseif vd==28 letter='2'; elseif vd==29 letter='3'; elseif vd==30 letter='4'; elseif vd==31 letter='5'; elseif vd==32 letter='6'; elseif vd==33 letter='7'; elseif vd==34 letter='8'; elseif vd==35 letter='9'; elseif vd==36 letter='0'; %******** elseif vd==37 letter='a'; elseif vd==38 letter='b'; elseif vd==39 letter='c'; elseif vd==40 letter='d'; elseif vd==41 letter='e'; elseif vd==42 letter='f'; elseif vd==43 letter='g';

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elseif vd==44 letter='h'; elseif vd==45 letter='i'; elseif vd==46 letter='j'; elseif vd==47 letter='k'; elseif vd==48 letter='l'; elseif vd==49 letter='m'; elseif vd==50 letter='n'; elseif vd==51 letter='o'; elseif vd==52 letter='p'; elseif vd==53 letter='q'; elseif vd==54 letter='r'; elseif vd==55 letter='s'; elseif vd==56 letter='t'; elseif vd==57 letter='u'; elseif vd==58 letter='v'; elseif vd==59 letter='w'; elseif vd==60 letter='x'; elseif vd==61 letter='y'; elseif vd==62 letter='z'; else letter='l'; %*-*-*-*-* end

5.5. Convert recognized letters to text format

% PRINCIPAL PROGRAM warning off %#ok<WNOFF> % Clear all clc, close all, clear all % Read image imagen=imread('testcheck1.jpg'); % Show image imagen1 = imagen;

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figure,imshow(imagen1); title('INPUT IMAGE WITH NOISE') % Convert to gray scale if size(imagen,3)==3 %RGB image imagen=rgb2gray(imagen); end % Convert to BW threshold = graythresh(imagen);

imagen =~im2bw(imagen,threshold); imagen2 = imagen; %figure,imshow(imagen2); % title('before bwareaopen') % Remove all object containing fewer than 15 pixels imagen = bwareaopen(imagen,15); imagen3 = imagen; %figure,imshow(imagen3); %title('after bwareaopen') %Storage matrix word from image word=[ ]; re=imagen; %Opens text.txt as file for write fid = fopen('text.txt', 'wt'); % Load templates load templates global templates % Compute the number of letters in template file num_letras=size(templates,2); while 1 %Fcn 'lines_crop' separate lines in text [fl re]=lines_crop(re); %fl= first line, re= remaining image imgn=fl; n=0; %Uncomment line below to see lines one by one %figure,imshow(fl);pause(2) %-----------------------------------------------------------------

spacevector = []; % to compute the total spaces betweeen % adjacent letter rc = fl;

while 1 %Fcn 'letter_crop' separate letters in a line [fc rc space]=letter_crop(rc); %fc = first letter in the line %rc = remaining cropped line %space = space between the letter % cropped and the next letter %uncomment below line to see letters one by one %figure,imshow(fc);pause(0.5) img_r = imresize(fc,[42 24]); %resize letter so that correlation %can be performed n = n + 1; spacevector(n)=space;

%Fcn 'read_letter' correlates the cropped letter with the images %given in the folder 'letters_numbers' letter = read_letter(img_r,num_letras);

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%letter concatenation word = [word letter];

if isempty(rc) %breaks loop when there are no more characters break; end end

%-------------------------------------------------------------------

% max_space = max(spacevector); no_spaces = 0;

for x= 1:n %loop to introduce space at requisite locations if spacevector(x+no_spaces)> (0.75 * max_space) no_spaces = no_spaces + 1; for m = x:n word(n+x-m+no_spaces)=word(n+x-m+no_spaces-1); end word(x+no_spaces) = ' '; spacevector = [0 spacevector]; end end

%fprintf(fid,'%s\n',lower(word));%Write 'word' in text file (lower) fprintf(fid,'%s\n',word);%Write 'word' in text file (upper) % Clear 'word' variable word=[ ]; %*When the sentences finish, breaks the loop if isempty(re) %See variable 're' in Fcn 'lines' break end end fclose(fid); %Open 'text.txt' file winopen('text.txt') clear all

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6. SAMPLE TESTING

During the process of execution, it selects a file from a directory using a dialog box.

Figure 6.1: Select an Image Dialog box

The image which has been selected will be displayed in the processing platform.

Figure 6.2: Show Image with error

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The processed image will be displayed as a text in notepad.

Figure 6.3: Text generation in a textbox

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7. PERFORMANCE TESTING

There are many types of tastings which can be performed in the software. One of the testing is

black box testing.

7.1 Black Box Testing In this type of testing the product design to perform is already known. The only thing to check

here is the input and its corresponding output. Out of millions of inputs we have consider only

four inputs.

Figure 7.1: Conversion of text from a single line image to text format

Figure 7.2: Conversion of text from a multi-line image to text format

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Figure 7.3: Conversion of text from a colored background image to text format

Figure 7.4: Conversion of text from a lower case letter format

Figure 7.5: Conversion of text from a lower case letter and upper case letter format

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Figure 7.6: Conversion of text from a distorted image to text format

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8. ARCHITECTURE

The Architecture of the Character Recognition and Conversion system on a grid infrastructure

consists of the three main components. They are:-

Scanner

Character recognition Software

Output Interface

Figure 8.1: CRC Architecture

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9. APPLICATION

Language Conversion

Along with English, there are varieties of languages which can be converted into user readable

format. By language translation software which uses DIA as its main functioning software.

Automatic Number Plate Recognition

Automatic number plate recognition is a mass surveillance method that uses optical

character recognition on images to read vehicle registration plates. They can use existing closed-

circuit television or road-rule enforcement cameras, or ones specifically designed for the task.

They are used by various police forces and as a method of electronic toll collection on pay-per-

use roads and cataloging the movements of traffic or individuals.

Data Entry for Business Documents

It is widely used as a form of data entry from printed paper data records, whether

passport documents, invoices, bank statements, computerized receipts, business cards, mail,

printouts of static-data, or any suitable documentation.

More applications,

More quickly make textual versions of printed documents

Make electronic images of printed documents searchable

Automatic insurance documents key information extraction

Converting handwriting in real time to control a computer

Assistive technology for blind and visually impaired users

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10. CONCLUSION

It is a powerful tool to recognize characters into electronic formats. As mentions earlier it can be used for

many purposes. However, the limited availability of capital could restrict the growth of this technology.

But if given proper enhancement it can be used for a variety of other purposes like, retinal scan,

recognition of new font and characters and can be used for storing large documents in electronic format

like one’s certificates and belongings.

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11. References

1. http://in.mathworks.com/help/vision/examples/recognize-text-using-optical-character-

recognition-ocr.html (6th Jan 2016, 19.14)

2. https://en.wikipedia.org/wiki/Optical_character_recognition(21st May 2016, 21.38)

3. http://www.advancedsourcecode.com/characterrecognition.asp(22nd may 2016, 22.13)

4. http://in.mathworks.com/help/vision/ref/ocr.html(21st May 2016, 20.46)

5. https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=

8&sqi=2&ved=0ahUKEwjx34-

Op5nKAhWPGI4KHSX3ALgQtwIILjAD&url=https%3A%2F%2Fwww.youtube.com%2

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h0p4&usg=AFQjCNEnGN163uiWbcciPic8ORNN6RHBRQ&bvm=bv.110151844,d.c2E(20t

h May 2016, 12.34)

6. Software Engineering, 9th Edition, IAN SOMMERVILLE

7. http://in.mathworks.com/help/matlab/predefined-dialog-boxes.html(20th May 2016, 15.45)

8. in.mathworks.com/help/matlab/ref/uigetpref.html(20th May 2016, 16.10)

9. http://in.mathworks.com/help/matlab/predefined-dialog-boxes.html(20th May 2016, 16.15)

10. http://in.mathworks.com/videos/creating-a-gui-with-guide-68979.html(21st May 2016, 8. 25)

11. http://in.mathworks.com/help/search.html?qdoc=use+a+selected+image+taken+by+uigetfile

&submitsearch= (21st May 2016, 8.45)

12. http://www.mathworks.com/matlabcentral/fileexchange/view_license?file_info_id=18169(21

st May 2016, 9.00)

13. http://www.mathworks.com/matlabcentral/fileexchange/18169-optical-character-

recognition--ocr-(21st May 2016, 10.30)

14. http://www.mathworks.com/matlabcentral/fileexchange/31322-optical-character-

recognition-lower-case-and-space-included-(21st May 2016, 11.00)

15. https://sourceforge.net/directory/os:windows/?q=character%20recognition%20using%20m

atlab(21st May 2016, 11.30)

16. http://www.caam.rice.edu/~timwar/CAAM210/OCR.html(21st May 2016, 12.00)

17. http://in.mathworks.com/help/vision/ref/ocr.html(21st May 2016, 12.15)

18. http://in.mathworks.com/help/vision/examples/recognize-text-using-optical-character-

recognition-ocr.html(22nd May 2016, 12.30)

19. http://www.springer.com/us/book/9781848003293(22nd May 2016, 16.00)

20. http://in.mathworks.com/help/images/(22nd May 2016, 16.15)