Image Processing - Optical Character Recognition

8/8/2019 Image Processing - Optical Character Recognition

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CS2K707(P)SeminarReport

on

IMAGEPROCESSING-OPTICALCHARACTERRECOGNITION

SubmittedInPartialFulfilment

OfTheDegreeOfBachelorOfTechnology

by

ROHAN

KAR

Y1.191,S7CSE

DepartmentofComputerScience

&EngineeringNationalInstituteofTechnology,Calicut2004Monsoon



NationalInstituteofTechnology,CalicutDepartment

ofComputerScience&Engineering

CertifiedthatthisSeminarReport

entitled

IMAGEPROCESSING-OPTICALCHARACTERRECOGNITION

isabonafide

reportoftheSeminarpresentedby

ROHANKAR

Y1.191,S7CSE

inpartialfulfilmentofthedegreeofBachelorof

Technology





Abstract

ImageProcessingis

nowadaysconsideredtobeafavoritetopicintheIT

industry.OneofitsmajorapplicationsisOpticalCharacterRecognition(OCR).Whenthe

objecttobematchedispresentedthenourbrainsoringeneralrecognitionsystemstartsextractingtheimportantfeaturesoftheobjectthatincludescolor,

depth,shape&



size.Thesefeaturesarestoredinthe

partofthememory.Nowthebrainstartsfindingtheclosestmatch

fortheseextractedfeaturesinthewholecollectionofobjects,whichisalready

storedinit.Thiswecanreferasstandardlibrary.

Whenitfindsthematchthenitgivesthematchedobjectorsignal

fromthestandard



libraryasthefinalresult.Forhumans

characterrecognitionseemstobesimpletaskbuttomakeacomputer

analyzeandfinallycorrectlyrecognizeacharacterisadifficulttask.Herewe

arebasicallydealingwiththeUpperCaseCharacter recognition.



Contents

1INTRODUCTION1

2SELECTIONOFTOPIC2

3WHATIS

RECOGNITION2

4MAINBODY2

5STEPSINVOLVED

INPATTERNRECOGNITION2

5.1INPUTTINGTHESTRINGSTOBERECOGNIZED..............3

5.2SEPERATIONOFCHARACTERS.........................3



5.3NORMALIZATIONOFINDIVIDUALCHARACTERS

.

.

.

.

.

.

.

.

.

.

.

.

..3

5.4THINNING......................................4

5.5SINGULARPOINTDETERMINATION......................

4

5.6GRIDFORMATION.................................6

5.7LINEDETECTION.................................7

5.8CHARACTERMATCHING.............................7

6APPLICATIONS7

7CONCLUSION



8

ii



1INTRODUCTION

DigitalImage

Processingisarapidlyevolvingfieldwiththegrowingapplicationsinscience

&engineering.ImageProcessingholdsthepossibilityofdevelopinganultimatemachinethat

couldperformvisualfunctionsofalllivingbeings.ThetermDigitalImageProcessinggenerallyreferstotheprocessingofatwo-dimensionalpicturebyadigital

computeri.e.altering



anexistingimageinthedesiredmanner

[1].Sincetheimageprocessingisavisualtask,theforemoststep

istoobtainanimage.Animageisbasicallyapatternofpixels

(pictureelements)thusadigitalimageisanarrayofreal&complexnumbersrepresentedbyfinitenumberofbits.

Themajortopics

ofImageProcessing



are:representation,processingtechnique&communication.Images

canbereadindifferentformatssuchas

JPEG(JointPhotographicExpertsGroup) TIFF(TaggedImageFileFormat) BMP

(WindowsBitmap) PCX(WindowsPaintbrush)OutoftheseformatstheJPEG,TIFF&BMParethefrequentlyusedformats.TIFFisanaturalfor

PC-basedImage



Processing.GoalsoftheTIFFspecificationare

extensibility,portability&revisability.

Afterwehavegotthebrief

ideaaboutImageProcessingletsmoveontotheapplications.ImageProcessinghas

gotabroadspectrumofapplicationssuchasRemoteSensingviaSatellitesandotherSpacecrafts,MedicalProcessing,Radar,Sonar,SpeechRecognition,Robotics,FaceRecognitionand

OpticalCharacterRecognition.



1



2SELECTIONOFTOPIC

CharacterrecognitionisaconceptrelatedtoDigitalSignalProcessingisquite

anupcomingtopicintheITbranch.Todaytheneedforrecognizingthe

signatureisamustinthebankinginstitutions.Thoughcharacterrecognitionisverysmallbutoneofthemostimportantpartinsignaturerecognition.It

beingthebasic



footageoftheconceptisveryimportant

inlogicdevelopment.DigitalSignalProcessingisaquiteahottopic

asitprocessesthesignalinthegraphicalmode,transformsittoa

digitalinput&hastheoutputinthedesiredform.Hence,turningontothepicturerecognitionwestartedonlywiththepatternrecognitionthat

tooconcentratingon



asmallerparti.e.characterrecognition.

3WHATISRECOGNITION

Recognitionistomapthe

givenpatternwithinternallystoreddatabase.Inotherwordsrecognitionisaprocess

ofmatchingthevisibleorthepresentedsignalwiththestandardsignal.Whenasignaloranyimageisinputtotherecognitionsystemit

extractstheimportant



featureslikethesize,depth,shape,color.

Thesefeaturesarestoredinthepartofmemory.Afterextractingthe

information,therecognitionsystemfindstheclosestmatchoftheinputsignalor

theimagewiththestandardlibraryofsignalsorimages.

Theprinciplebehindtheproposedmethodofrecognitionisthatthebasicgeometry

ofapattern



orcharacterisretainedevenafterdeformations.

Ifthesegeometricalfeaturescanbeextractedfromtheinputcharacterthen

thecharactercanbematchedwiththestandardcharactersinthelibrary.

4MAINBODY

OCRistheprocessofconvertinganimageoftext,suchasascannedpaperdocumentorelectronicfax

file,intocomputer-editable



text.Thetextinanimageis

noteditable:thelettersaremadeoftinydots(pixels)thattogether

formapictureoftext.DuringOCR,thesoftwareanalyzesanimageand

convertsthepicturesofthecharacterstoeditabletextbasedonthepatternsofthepixelsintheimage.

5STEPSINVOLVEDIN

PATTERNRECOGNITION



Firstofallwescanthe

inputstring.Thenweseparatethestringintocharacters.Thiswecan

termasobjectalignmenti.e.wehaveourobjectreadyforindividualprocessing.

Nextstepisthefeatureextraction.Thefeaturesthatcanbedetermined&relieduponemphaticallyaretobeextracted,sowecategorize

thosefeaturesinto



Numberoflines&

theirdirections. NumberofSPsalongwiththeirpositions.Alsoimplemented

isathinningalgorithmsothattheredundantinformationwillbededucted&

neededwillbesavedforflawlessrecognition.Thegridsystemisalsointroducedwhichenablesustocovertheminutedetailsofthecharactersof

anysize.For





5.1INPUTTINGTHESTRINGSTOBE

RECOGNIZEDThedocument,whichmaybethehandwrittenstatementsormachine

printedstatementisscannedwiththehelpofscanner.Thenthescanneddocument

issavedeitherinBMPorTIFFformatsasspecifiedinthevariousformatlistearlier.Thescanneddocument,whichissavedineitherof

theformats,is



openedintheOCRsoftware.Nowthe

nextjobistoselectthewordfromthescanneddocument.The

OCRsoftwareperformsthisjob.Theselectedwordisnowavailableforfurther

processing.Nowthenextjobistheseparationofcharacters.

5.2SEPERATIONOFCHARACTERSThebasicideabehindtheseparationofcharacteris

tosearchthe



blankspacesbetweentheindividualcharacters.When

theselectedwordisscanned,rightfromthefirstcharactertheneach

timeitwillcomeacrossapixeli.e.tinydotswhichindicatethe

presenceofapartofcharacter.Thiswillcontinuetillitgetsthetwoorthreecontinuousblanks,whichistheindicationoftheend

ofasingle



character.Inthiswaythecharacterscan

beseparated.Theindividualcharactersarethengiventothenextstate

asinput,whichisbasicallyanormalizationstep.

5.3NORMALIZATIONOF

INDIVIDUALCHARACTERSThecharacterswrittenbyhandvarygreatlyinsizeandshape.Toaccountforvarietyinshape,thecharacterisnormalized.Bynormalization,

wemeanthat



thecharacterismadetofitinto

astandardsizesquare.Thisisaccomplishedbyfillinga2-Darray

by1sor0sdependinguponthepresenceorabsenceoftextpixel

inthatparticulararea.Thissizeofarrayischosenbytrialanderrormethod&thevaluethatgivesthebestresultsisfixed.

Charactersofany



sizeandshapecanbeprocessedand

matchedwiththenormalizationtechnique.

Lettheheight&width

ofthecharacterish &w respectively.Thusnormalizingfactorxnormal

andynormalinX&Ydirectionsaregivenby

xnormal=arraysize/w ynormal

=arraysize



/hThenifthepixelon

thescreenhastheco-ordinatesx andy relativetothetop

leftcorneroftheboundingrectangleofthecharacter,theXandY

co-ordinateofthatpixelinthestandardsizearrayare

x=x *xnormal y=y *

ynormalThus



thefinalresultisthe2-Dinteger

arrayi.e.thestandardapproximationofthecharacter.Ifthecolorof

thepixelonthescreenisnotthesameasbackgroundcolorthen

thecorrespondingarrayelementisfilledas1 elseitisfilledas0.Thenormalizationcanbebrieflyrepresentedbythefollowingfigure

3





.

Figure1:

NORMALIZATIONOFCHARACTERA

5.4THINNINGThinningalgorithmisapplied

toallnormalizedcharacters.Whenthecharacterisnormalized,someredundant1sare

alsostoredinthestandardarray.Thisaffectstheparametersofthecharacteri.e.thickness.Thinningreducesthethicknessofthecharacterstotheiractual

skeletonaswell



asremovesalltheredundantoneswith

thehelpofeightstandarddeletionmasksi.e.twoforeachdirection

(top,bottom,left&right).Thelinesformingthecharactersreducetoa

widthofonepixel.

Howeverbydeletionoftheextremepixels,someimportantpixelsmayalsobedeletedthatmayresultthediscontinuity

oftheimage.



Hencefournon-deletionmasksareused.If

anyofthefourmasksissatisfiedthenthepictureisretained.

ThinningalsohelpsindetectionofSingularPoints&lines.

5.5

SINGULARPOINTDETERMINATIONSingularpointcanbedefinedasanypointwithadegreegreaterthattwo.Thus,SPsarethosepointswherebranchingoccurs

asexplainedby



thefollowingfigure

4



Figure2:SINGULARPOINTINCHARACTER

A

Thedegreeofapointisequaltothe

numberoflinesemergingfromthatpoint.Ifthepointisonline,

thedegreeis2&ifthepointisendpointofaline,thenitsdegreeis1.Howeverforapointwherebranching

occursthedegree



is3orgreater.TheseSPsare

importantcharacteristicsinaparticularcharacter,andtheirinformationcanbeused

todifferentiatebetweendifferentcharacters.TodeterminewhetherthepointisSPor

not,itisnecessarytofindoutthetotalnumberoflinesoriginatingfromthepoint.Todosowecheckthepixelsin5

by5neighborhoods



asshowninthefigurebelow-

Figure3:5BY5NEIGHBOURHOODOFTHEPIXELUNDERCONSIDERATION

Herewecheckforthetotalnumberof0to1

transitions.Ifthe0to1transitionsaremorethan2,itindicatesthatthenumberoflinesfromthatpointarealsogreaterthat

two.Henceany





5.6GRIDFORMATIONInanycharacter

therearecertaingeometricalsimilarities.Thesepropertiesarelocaltotheparticular

area.ForexampleifweconsiderthealphabetA,thetopmostpointis

avertexwithtwolines.Similarlyatriangleisformedbythreelines.Howeverwhenweconsiderthehandwrittencharactersitispossiblethat

thesepropertiesmight



beslightlydisplaced.Butthepropertieswill

occurclosetotheexactpositions.Bygridformationweaimto

collectinformationabouttheseproperties&theirrelativepositions.

Figure

4:DIRECTIONOFPIXELSFROMPIXELUNDERCONSIDERATION

Figure5:STANDARDARRAYDIVIDEDINTOGRIDS

Ingridformationthestandard

arrayisdivided



intoninesquaresorgrids.Referthe

figureonbackpage.Thesegridsarethenanalyzedtofindout

thenumberoflines&SPs.Eachgrid

6



willhaveasetoflines

&orsingularpoints.Ifnoneofthesecharacteristicsarepresentthen

thevariablesforthatparticulargridwillbesetto0.Thusits

lines&SPswillcharacterizeeachgrid.

5.7LINEDETECTIONLinedetectionisdoneonthebasisofdirectionflagssetforeach

pixelinthe



grid.Thedirectionflagsarefirstset

foreachpixelbyanalyzingtheneighboringpixelsofthecurrentpixel.

Thedirectionsarenumberedfrom1to8asshowninthefigure

inthebackpage.

Todetectthelinesthedirectionflagsassignedtoeverypixelarecounted&thedirectionthathasmajority

ofthepixels



isassignedtotheline.Thismethod

notonlygivesthenumberoflinesinthegridbutalso

directionofeachline.Ifthegriddoesnthaveanypixelitis

saidtohavenolines.

5.8CHARACTERMATCHINGThelaststepinOCRisthematchingofthescannedinputcharacterwiththe

standardone.The



charactersarerepresentedasasetof

ninesetsofparameters,onesetcorrespondingtoagrid.Thuseach

gridwillhaveitsownsetofparameters.SET1:

1.Numberoflinesinthatgrid. 2.NumberofSPsinthatgrid. 3.Directionofeachofthelines.SET2:

1.Nine



gridstructuresoftheformofset1.

2.Theactualcharacter. 3.TotalnumberofSPsin

thecharacter. 4.Totalnumberofgridsmarkedasimportant. 5.

Gridnumberoftheimportantgrids.Theabovedataisretrievedfromtheinputcharacter&itismatchedwiththeinbuiltlibraryofcharacters.

Allcharactersin



thelibraryalsohaveabovestructureformat.

Thematchingprocessisdoneonthepointbasisi.e.whenevera

characteristicoftheinputcharactermatcheswiththatofthelibrarythepoints

forthatcharacterareincreased.Thegridsareverycrucialinthematching.Ifthecharacteristicsofthegridsdontmatchthepointsofthe

characteraremade



0.Intheendthecharacterscoring

themaximumnumberofpointsisselectedastherecognizedcharacter.

6APPLICATIONS

OCRcanbeusedasaninputto

anysoftwarewhichrequiresthewrittendatatobeprocessedsuchascreditcards,banking&otherfinancialinstitutionsthathavealotofinteraction

withthepeople.



OCRcanalsobeusedtoconvert

handwrittenmaterialintocomputerfonts,fordocumentation.Inthebankingsector,for

cheques,signaturerecognitionandothertransactions,OCRcanbeusedtodirectlyfeed

theinformationtothecomputerwithouthavinganyonetomanuallytypeit.

7



7CONCLUSION

Fromall

theaboveexplanations,itisobservedthatthoughthecharacterrecognitionis

averysmallpartofaveryvastfieldofDigitalSignalProcessing,

itisconsideredtobeaboontothebankinginstitutionsforsignaturerecognition.Theotherapplicationsincludepatternrecognitionofthescanneddigitalimages

fromthesatellite



andcomparingthemwiththepreviousimages.

Bydoingthis,thepredictionabouttheclimatecanbemadeeffectively.

Thiscanbeimplementedwithaproperaccuracygivingrichdividendsinbranch

ofbanking,satellitecommunicationaswellasinotherinstitutionswheresuchsignatureoranycharacterrecognitionisnecessary.

8



References

[1]http://slt.wcl.ee.upatras.gr/papers/kavallieratou4.pdf[2]

http://slt.wcl.ee.upatras.gr/papers/maragoudakis14.pdf[3]http://www.kornai.com/MatLing/ocrfinal.pdf[4]http://www.ecse.rpi.edu/homepages/nagy/PDFfiles/Nagy-SPIE3967-2000.pdf9



Documents

Image Processing - Optical Character Recognition