Guide to OCR for Arabic Scripts

592 Pages·2012·19.587 MB·English

by  Sargur N. Srihari, Gregory Ball (auth.), Volker Märgner, Haikal El Abed (eds.)

Guide to OCR for Arabic Scripts
Volker Märgner (cid:2) Haikal El Abed
Editors
Guide to OCR
for Arabic Scripts
Editors
VolkerMärgner HaikalElAbed
InstituteforCommunicationsTechnology InstituteforCommunicationsTechnology
BraunschweigTechnicalUniversity BraunschweigTechnicalUniversity
Braunschweig,Niedersachsen Braunschweig,Niedersachsen
Germany Germany
ISBN978-1-4471-4071-9 ISBN978-1-4471-4072-6(eBook)
DOI10.1007/978-1-4471-4072-6
SpringerLondonHeidelbergNewYorkDordrecht
LibraryofCongressControlNumber:2012941223
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Foreword
ItiswithgreatpleasurethatIacceptedtowritetheforewordforthisbook.
In our society, we use computers increasingly for all kinds of communication.
This means that for a language to work well for communication, we need to have
computer tools—language technology—for that language. Language technology
makescommunicationmoreefficient,anditenablesmanymoreuserstohaveaccess
toinformationinvariousforms.
Arabic is an important language, spoken in a large region of the world, and it
isimportanttosupporttheuseofArabic—inbusiness,inpublicadministration,in
privatehomes,andevenintheexchangeofinformationwiththerestoftheworld.
AsnotallArabicinformationisalreadyavailableinelectronicform,oneofthe
basic requirements in such a scenario is the ability of computers to “read” images
of written Arabic, be it handwritten or printed, be it online or offline. This kind
of technology (OCR—optical character recognition) is difficult to create even for
languagesinLatinscript,andwrittenArabicaddstothecomplexity.
This book is a comprehensive guide to the field of Arabic OCR, offering thor-
ough descriptions of the data sets for training and testing, several different OCR
methodologies,andtheevaluationandassessmentthereof.
Beforeclosing,Iwouldliketocongratulatetheeditorsinhavingassembledsuch
animportantcollectionofcontributionstoacomplexproblemforwhichtheArabic
world,andtherestoftheworld,needssolutions.
Copenhagen,Denmark BenteMaegaard
v
Preface
ArabicScripts
The Internet is a source of information which is used by approximately 2 bil-
lionusersworldwide(www.internetworldstats.com).Twoaspectsaffectthewayin
whichtheInternetisused,especiallyinsearchingfordocuments.Oneistheavail-
abilityofmoreandmorescanneddocumentswithvaryingamountsofmetadatafor
searchingthesedocumentsontheInternet,andtheotherostheappearanceofmore
andmorelanguageswithnon-Latincharacters.Bothaspectsshowtheimportanceof
developingrecognitiontechnologyforalltypesofcharactersandlanguagestomake
thecontentofscannedimagesoftextavailabletotheInternetusers.Aworldwide-
used acronym for any type of text recognition is OCR, which means optical char-
acterrecognition.OCRisusednotonlyforrecognizingprintedcharacters,butitis
oftenalsousedforcursivehandwriting,evenwhenwordsinsteadofsinglecharac-
tersarerecognized.Somealternativeacronymsareusedforthecaseofhandwritten
words,likeHWR(handwrittenwordrecognition)butthesearenotincommonuse
today.
Knowingthatabout200millionpeopleintheworlduseArabicastheirfirstlan-
guage it is obvious that a growing interest of that huge group of Arabic-speaking
Internetusersistosearchfordocumentsintheirmothertongue.Inparalleltothis
situationisinthepastfewyearsagrowinginterestinArabicwordandtextrecog-
nition has been observed. During that time two events have been important land-
marks in Arabic text recognition technology development. In 2002 a database on
Arabic handwritten words (IFN/ENIT-database)1 was made available to the com-
munityandhasservedasareferenceforcompetitionssince2005(ICDAR2005).2
InSeptember2006asummitonArabicandChineseHandwritingRecognitionwas
held at College Park,MD in the USA (SACH2006),3 where experts from both re-
1http://www.ifnenit.com
2http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10526
3http://www.umiacs.umd.edu/lamp/meetings/SACH06/
vii
viii Preface
Table1 Arabiccharacters(www.ethnologue.com)
Fig.1 ExampleofanArabic
printedtext
searchfieldspresentedtheiractualwork.FromthattimeintensiveresearchonAra-
bicscriptrecognitionstartedandhasresultedinabigstepforwardtoday.
Arabicscriptisthesecondmostwidespreadscriptintheworld;itisusednotonly
forArabicbutalsoforthePersian,Urdu,andPashtolanguages,forexample.Today
14languagesuseArabicscriptworldwide,whichshowsitsimportance.Character-
isticsofArabicscriptareawritingdirectionfromrighttoleft,characterswithina
wordbeingmostlyconnected,28characterswithdifferentshapesfordifferentpo-
sitionsinaword,anddotsanddiacriticalsignsaboveandbelowcharacters.Table1
showsalltheshapesofthe28Arabiccharacters.
Fordifferentlanguagessomeadditionalcharactersmaybeused.TypicalforAra-
bic script is also the variation of a word in length by elongationof the connecting
linesbetweenthecharacters.Figure1showsanexample.
Preface ix
Table2 Examplesof
ligatures
AfurtherimportantspecialArabicscriptstyleisthepossibilitytowritecharac-
tersasverticalorhorizontalligatures.Theseligaturesmodifytheshapeofthechar-
acterssignificantly.SomeexamplesofligaturesareshowninTable2.Allthesetyp-
icalArabicscriptcharacteristicsinfluencetheprocessingandrecognitionofArabic
scriptindifferentwaysandmakeitclearthatasimpleadaptationofLatincharacter-
basedprocessingisnotpossible.
ThisbookpresentsthestateoftheartofOCRforArabicscriptspresentedfrom
mostactiveandsuccessfulgroups.Thepartsofthebookshowthatalotofworkstill
hastobedoneonArabicscriptrecognition.Butthetechniquesandalgorithmsused
areofgeneralinterest;manyproblemsaretypicalnotonlyforArabicbutformany
otherscripts.WebelievethatthecollectionofArabicOCRrelatedworkisalsoan
inspirationforotherscriptsandviceversa.
The book is divided into four parts. Part I, Pre-processing, presents different
aspects of pre-processing and feature extraction for Arabic OCR systems. Part II,
Recognition,includeschapterswithdetailsaboutdifferentrecognitionapproaches.
PartIIIcollectschaptersdescribingtheimportantaspectsofhowtoassesstheper-
formanceofarecognitionsystem.ThefinalPartIV,Applicationspresentssystem
solutionsforselectedapplicationfields.
PartI:Pre-processing
Part I presents different approaches for the pre-processing of OCR systems for
Arabic. It starts with an overview of Arabic handwriting recognition technology.
Srihari and Ball present in their chapter the parts of a recognition system from
pre-processing to classification. Finally they discuss application fields and chal-
lenges. Chapters 2–6 deal with pre-processing tasks of an OCR system. Bukhari,
Shafait, and Breuel discuss layout analysis methods, Setlur and Govindaraju pre-
processingissues,BelaidandOuwayedsegmentationofancientArabicdocuments,
andLikforman-Sulemetal.featuresforwordrecognitionsystems.
PartII: Recognition
Chapters7–15presentdifferentapproachesfortherecognitionofArabicscript.The
firstsixchaptersalluseHMM-basedapproaches.BorovikovandZavorinpresenta
multi-stageapproachtodocumentanalysis,Ahmed,Mahmoud,andParvezarecog-
nizerforprintedArabictext,Pechwitz,ElAbed,andMärgneranofflinehandwritten
x Preface
Arabicwordrecognizer,Dreuw,Rybach,Heigold,andNeyalargevocabularyop-
ticalcharacterrecognitionsystem,Alkhoury,Gimenez,andJuanaBernoulli-based
handwriting recognition system, Jifroodian and Suen a handwritten Farsi word
recognitionsystem,Kessentini,Paquet,andBenHamadouamulti-streamMarkov
modelrecognizer.
Two chapters discuss further approaches. Graves presents a recognition system
basedonmultidimensionalrecurrentneuralnetworks,andMozaffaridiscussesthe
application of fractal theory for document analysis and recognition. Khemakhem
andBelghithdiscussanOCRsystembasedonthecombinationofcomplementary
systemsinChap.15.
PartIII:Evaluation
The subject of Part III is the evaluation of recognition systems. In Chap. 16 Za-
vorin and Borovikov discuss data collectionand annotation, and Arabic handwrit-
ingrecognitioncompetitionsaredescribedinChap.17byMärgnerandElAbed.In
Chap.18,Slimaneetal.describebenchmarkingstrategiesforArabicwordrecogni-
tion.
PartIV:Applications
The final Part IV presents different applications using Arabic script recognition
technology. In Chap. 19 Cheriet and Moghaddam present a robust word spot-
ting system for historical Arabic manuscripts. Natarajan discusses, in Chap. 20,
script-independent methods for Arabic handwriting recognition, and Kundu and
Hines present an Arabic handwriting recognition system using over-segmentation
inChap.21.Boubakeretal.discussonlineArabicdatabasesandapplicationsusing
thesedatainChap.22,andAbdelazeemetal.present,inChap.23,techniquesfor
usingonlineandofflinefeaturesforArabichandwritingrecognition.
TargetAudience
Thisbookprovidesanoverviewofthestate-of-the-artresearchinthefieldofOCR
for Arabic scripts. Different aspects and solutions have been addressed by the au-
thors,andwehopethatthiscomprehensivecollectionofideas,problems,andsolu-
tionsmotivatesresearcherstocontinuethiswork.Inthatsensethisbookshallserve
asareferenceforresearchersandgraduatestudentsstudyingOCRtechnologyand
methodologyingeneralandforArabicscriptinparticular.
Braunschweig,Germany VolkerMärgner
HaikalElAbed
Acknowledgements
We thank all the chapter authors for their contributions to this book, which make
it an invaluable resource to researchers in the area of OCR for all kinds of script
using Arabic characters. A total of 67 authors prepared their contributions for the
23chaptersofthebookandsupportedusduringallstepsfromconcepttodraftand
finalized chapter. We would like to thank Bente Maegaard, Director of the Cen-
tre for Language Technology at Copenhagen University, Denmark, for writing the
foreword and for her continuing support in developing Arabic language resources
andanetworkofresearchersinthisfield.WewouldalsoliketothankSpringerfor
theirencouragementandpersistenceindrivingustocompletethisworkinatimely
manner.
Braunschweig,Germany VolkerMärgner
HaikalElAbed
xi
Contents
PartI Pre-processing
1 AnAssessmentofArabicHandwritingRecognitionTechnology . . . 3
SargurN.SrihariandGregoryBall
2 LayoutAnalysisofArabicScriptDocuments . . . . . . . . . . . . . 35
SyedSaqibBukhari,FaisalShafait,andThomasM.Breuel
3 AMulti-stageApproachtoArabicDocumentAnalysis . . . . . . . . 55
EugeneBorovikovandIlyaZavorin
4 Pre-processingIssuesinArabicOCR . . . . . . . . . . . . . . . . . . 79
ZhixinShi,SrirangarajSetlur,andVenuGovindaraju
5 SegmentationofAncientArabicDocuments . . . . . . . . . . . . . . 103
AbdelBelaïdandNazihOuwayed
6 FeaturesforHMM-BasedArabicHandwrittenWordRecognition
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Laurence Likforman-Sulem, Ramy Al Hajj Mohammad, Chafic
Mokbel,FaresMenasri,Anne-LaureBianne-Bernard,andChristopher
Kermorvant
PartII Recognition
7 PrintedArabicTextRecognition . . . . . . . . . . . . . . . . . . . . 147
IrfanAhmed,SabriA.Mahmoud,andMohammedTanvirParvez
8 HandwrittenArabicWordRecognitionUsing
theIFN/ENIT-database . . . . . . . . . . . . . . . . . . . . . . . . . 169
MarioPechwitz,HaikalElAbed,andVolkerMärgner
9 RWTHOCR:ALargeVocabularyOpticalCharacterRecognition
SystemforArabicScripts . . . . . . . . . . . . . . . . . . . . . . . . 215
PhilippeDreuw,DavidRybach,GeorgHeigold,andHermannNey
xiii