[机械模具数控自动化专业毕业设计外文文献及翻译]【期刊】一种视频文字字符多方位分割的新方法-外文文献

A New Methodfor CharacterSegmentationfromMulti-OrientedVideo WordsNabin Sharma, Palaiahnakote Shivakumara, UmapadaPal, MichaelBlumensteinand Chew Lim TanGriffith University, Queensland,Australia4222. Email:nabin.sharma,.auUniversity of Malaya(UM), Kuala Lumpur-50603,Malaysia.Email: .myCVPR Unit, Indian StatisticalInstitute,Kolkata, India 700108.Email: umapadaisical.ac.inSchoolof Computing,NationalUniversity of Singapore,Singapore.Email: .sgAbstractThis paper presents a two-stage method for multi-oriented video character segmentation.Words segmented fromvideo text lines are considered for charactersegmentationin thepresent work. Words can contain isolated or non-touchingchar-acters, as well as touching characters.Therefore, the charactersegmentationproblem can be viewed as a two stage problem.In the first stage, text cluster is identified and isolated (non-touching) characters are segmented. The orientation of eachword is computedand the segmentationpaths are found in thedirection perpendicularto the orientation.Candidatesegmenta-tion points computed using the top distance profile are used tofind the segmentationpath between the characters consideringthe background cluster. In the second stage, the segmentationresults are verified and a check is performed to ascertainwhether the word component contains touching characters ornot. The average width of the components is used to find thetouchingcharactercomponents.For segmentationof the touchingcharacters, segmentation points are then found using averagestroke widthinformation,alongwith the top and bottomdistanceprofiles. The proposed methodwas tested on a large dataset andwas evaluated in terms of precision, recall and f- measure. Acomparative study with existing methods reveals the superiorityof the proposed method.Keywords: Video Document Analysis, Video Character Seg-mentation, Multi-orientedDocument Processing,Video CharacterRecognition,Piece-wiseLinear SegmentationLine (PLSL).I. INTRODUCTIONContent-basedindexing and retrieval of videos is becom-ing more essential due to the increasing size of multimediadatabases. Text present in videos plays an important role invideoindexing and retrieval and videotext has been classifiedinto two groups 2, 3 namely Scene text (e.g. text onvehicles, commodities,buildings, sign boards on roads, etc.)and Graphic text or Captiontext (news video,sportsvideo,etc). Hence both types of text can be extremely useful inthe effective indexing and retrieval of the videos. The majorchallengesin text informationextraction from video are lowresolution, complex/non uniform backgrounds and blur, tomention a few. Though the text extractionstep segments thetext region, but still it may contain considerableamounts ofnon-text portions,whichconsequentlyhamperscorrectOpticalCharacterRecognition(OCR). Minimizingthe effect of non-text backgroundhas a high potential in improving the OCRresults.Hence,word and charactersegmentationaimsto refinethe actual text area by reducing the non-text background.Word segmentationactually divides the text line/region intosmallerregions,whichin turn reducesthe interactionwith thebackground noise. Whereas, character segmentation reducesthe words into further smaller regions comprising of singlecharacters, thereby further minimizing the non-text portion.The segmented characters are then sent to the OCR enginefor recognition.The problembecomesmorechallengingwhenthe words are multi-orientedin video. Hence in this paper,a new methodfor charactersegmentationfrom multi-orientedvideowords is proposed.Words extractedfrom multi-orientedand horizontalstraight lines from video were consideredforexperiments.A comprehensive survey of character segmentation tech-niqueswas presentedby Casey and Lecolinet1. Three mainapproacheswherementionedin the survey, namely, dissection-based,recognition-basedand holisticsegmentation.Recentlyafew techniquesfor charactersegmentationfrom video frameshave been reported in the literature 7, 9, 8. Phan et al.7 proposeda GradientVector Flow (GVF) based techniquefor video charactersegmentation.The authors used GVF forthe identificationof cut candidatesand framed characterseg-mentationas a minimumcost path findingproblem.The inputimage is consideredas a graph where pixels are consideredvertices connected to their neighboringpixels. Rajendran etal. 9 used Fourier-Moments features to extract the wordsand charactersfrom videotext lines.The authorsreliedon thefeature which specified that the text height difference at thecharacterboundarycolumnis smallerthan the other columns,but the samemay not be true in caseswherethe text is slantedor is in italics. The same idea was also used by Shivakumara8 to segmentcharacters,but the authorsused gradient-basedfeatureswith Max-Minclusteringto obtain the text clusters.The presenceof touchingcharactersdue to poor resolution,blur and backgroundnoise is a major bottle neck in charactersegmentation and OCR of video text. Hence, a two-stageapproachis proposed,whereinfor the stage-I, the isolatedornon-touchingcharactersaresegmentedandsubsequentlystage-II concentratesontouchingcharactersegmentation.Inspiredbythe workreportedin 7,wherethe charactersegmentationwasformulatedas a minimumcost path findingproblem,we pro-pose the use of Piece-wiseLinearSegmentationLines(PLSL)for segmentingcharactersin both stages.Distanceprofilefea-turesare usedin stage-Ito estimatethe candidatesegmentationpoints. Distance profile features along with stroke width andaverage width of the characters are used to determine thecandidatetouchingsegmentationpoints.The main advantagesof the proposed method is that it not only allows a curvedsegmentationpathwhenrequired(incaseof multi-orientedandtouching character) but also does not require any thresholdsin order to classify the character gaps. Unlike 7 where acost functionis used to guide the segmentationpath, the textand non-text clusters are used as guides for the PLSLs in2013 12th International Conference on Document Analysis and Recognition1520-5363/13 $26.00 2013 IEEEDOI 10.1109/ICDAR.2013.90413the proposedmethod,whichis computationallyless expensivethan the use of a cost function.In the presentwork, candidatesegmentationpoints are found rather than trying to find thesegmentationpath at a fixed interval, as proposedin 7. Theuse of fixed interval to find the segmentation path betweencharactersin 7 resultedin morefalse positives, whichin turncreates an overhead for a dedicatedfalse positive eliminationstep. Whereas,in the proposedmethodfalse positives seldomoccurred in stage-I and considerably less occurred duringtouchingcharactersegmentation.The rest of the paper is organized as follows. The pro-posed character segmentation method is detailed in SectionII. Section III presents the experimentalresults, comparativestudy with the existing methodsand discussionon the resultsobtained as well as failure cases. Section IV concludes thepaper providing the scope for future work.II. PROPOSEDCHARACTER SEGMENTATIONMETHODOLOGYThe word segmentationmethod proposedin 4 was usedto segmentthe wordsfroma videotext line detectedusing5.The resultingword imageis the inputfor the proposedcharac-ter segmentationmethod.Theproposedmethodwhichconsistsof two stages is discussed in the sub-sectionsgiven below.The method for candidate text cluster selection is describedin Section IIA. The candidate segmentation point selectionand charactersegmentationtechniqueare describedin the sub-section IIB. A detailed descriptionof the touching charactersegmentation and false positive elimination techniques aregiven in sub-sectionIIC.A highlevel overview of theproposedmethodis presentedin the flow chart shown in Figure 1.A. Candidatetext cluster selectionThe input word image is a color image having a non-uniform background with noise. Hence, it is essential toidentify the text and non-text pixels. In order to over comethe problem with non uniform backgrounds a Differenceof Gaussian filter is applied to remove the low frequencybackgroundblobs. The text/non-text clustering is performedusing min-max clustering 8. The minimum and maximumgray values are calculatedfor min-max clustering.Once theminimum(Cmin) and maximum(Cmax) clusters are found,a text cluster is then identified.It is generally observed thatthe pixels near the image border or outside the minimumboundingbox of the word belong to the backgroundcluster.Hence,the numberof pixels minborderbelongingto theCminand maxborderto Cmaxclusterare calculatedconsideringtheborder rows and columnsof the word 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1: Flow diagramof the proposedcharactersegmentationmethodIf minbordermaxborder, then Cminis consideredas thebackgroundcluster else Cmaxis consider as the backgroundcluster. Once the text cluster is found ConnectedComponentAnalysis(CCA)is applied,and the very smallcomponentsareremoved as they usually representthe backgroundnoise. Thesampleword imagesand the identifiedtext clustersare shownin Figure 2. Both clusters are used to segment charactersinthe later steps of the method.B. Stage-I: Non-touching character segmentationStage-Iof theproposedmethodconcentratesonsegmentingnon-touchingcharacters.Text and non-text clusters identifiedare used as guides to find the piece-wiselinear segmentationlinebetweencharacters.Thefollowingarethestepsto segmentthe isolatedcharacters.1) Candidatesegmentationpointselection: In-orderto findthe candidate segmentation points, orientation of the textcluster is first estimated followed by the estimation of thetop distanceprofile.The orientationof the word is calculatedusinga PCA-basedapproach6.For computationalsimplicity,words having orientationless than a thresholdTdeg(derivedempiricallyandconsideredas15) areconsideredas horizontalotherwisemulti-oriented.As multi-orientedwords are consid-eredfor the experiments,the candidatesegmentationpointsarefound in the directionperpendicularto the orientationof theword,insteadof rotatingthe word imageto make it horizontal.To avoid an extra operationfor rotationand its drawbacks,themulti-orientedwords were processedin their originalform. Incase of multi-orientedwords, the minimumboundingbox isconsideredand a distance profile calculated in the directionperpendicularto its orientation.Consideringhorizontalwordas an example,for eachcolumnin the word image,the top distanceis the distancebetweenthetopmostpixel and the first text pixel in the column.A sampletop distance profile graph is shown in Figure 2(a). It can beseen that the graph containsmany local peaks, some of themrepresentcleargapsbetweencharactersas the distanceis equalto the heightof the word. In the case of slantedor italicwordsit is not possibleto find a distancepeak which is equal to theheight of the word. In such cases the distance is less thanthe height but there are high peaks between the characters.The top distance profiles shown in Figure 2(c) explains thescenario of non-horizontaland slanted words. Once the topdistanceprofilesare calculated,the localpeaks(marked in red)as shown in Figure 2, are found and consideredas candidatesegmentationpoints.2) Character segmentation: The candidate segmentationpoints found are used to segment charactersusing the piece-wise linear segmentation lines. The proposed method is in-spired by 7, where the character segmentation problem isformulatedas a minimumcost path findingproblem.Using acost functionand calculatingthe cost at each instanceis com-putationallyexpensive, hence non-text clustersare consideredin the proposedmethod. Starting from a candidatepoint, themethodtries to move towards the other boundaryof the wordimage using the non-text (CNT) cluster. If the orientationofthe word is horizontal,the bottom five neighboringpixels asshown in Figure2(b) i.e. pixels p1(x+1,y), p2(x+1,y1),p3(x+1,y+1), p4(x,y1) and p5(x,y+1)are consideredto find piece-wiselinear lines. In the case that all five pixels414;#23;#23#23;#23#23#23;#23#23#23#23;#23#23#23#23#23;#23#23#23#23#23#23;#23#23#23#23#23#23#23;#23#23#23#23#23#23#23