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J. Imaging 2018,4, 43
Negative RateMetric (NRM): NRM is defined from the negative rate of false negative (NRFN)
(Equation(6))andthenegativerateof falsepositive (NRFP) (Equation(7)):
NRFN= FN
FN+TP (6)
NRFP= FP
FP+TN (7)
TN, defined as true negative, occurswhen both the image pixel and ground truth are labeled as
background. ThedefinitionsofTP,FN, andFPare thesameas theonesgivenfor theF-Measure.
NRM= NRFN+NRFP
2 (8)
AlowerNRMindicatesabettermatch.
4.2. TextLineSegmentation
4.2.1.Datasets
Thepalmleafmanuscriptdatasetsfortext linesegmentationtaskarepresentedinTable2. Thetext
linesegmentationgroundtruthdata forBalineseandSundanesemanuscriptshavebeengenerated
byhandbasedon thebinarizedground truth images [17]. ForKhmer1, a semi-automatic scheme
isused [26,59]. A set ofmedialpoints for each text is generatedautomaticallyon thebinarization
groundtruthof thepage image. Then thosepoints canbemovedupordownwitha tool tofit the
skewandfluctuationof thereal text lines.Wealsonote touchingcomponentsspreadingovermultiple
linesandthe locationswhere theycanbeseparated. ForKhmer2and3,anIDof the line itbelongs to
isassociatedwitheachannotatedcharacter. Theregionofa text line is theunionof theareasof the
polygonboundariesofall annotatedcharacterscomposing it [21,27].
Table2.Palmleafmanuscriptdatasets for text linesegmentationtask.
Manuscripts Pages TextLines Dataset
Balinese1 35pages 140 text lines ExtractedfromAMADI_LontarSet [17,26,40]
Balinese2 Bali-2.1:
47pagesBali-2.2:
49pages 181 text lines
182 text lines ExtractedfromAMADI_LontarSet [17]
Khmer1 43pages 191 text lines ExtractedfromEFEO[20,26,59]
Khmer2 100pages 476 text lines ExtractedfromSleukRithSet [21,27]
Khmer3 200pages 971 text lines ExtractedfromSleukRithSet [21]
Sundanese1 12pages 46 text lines ExtractedfromSundaDataset [26]
Sundanese2 61pages 242 text lines ExtractedfromSundaDataset [22]
4.2.2. EvaluationMethod
Followingourpreviouswork[26],weusetheevaluationcriteriaandtoolprovidedbyICDAR2013
HandwritingSegmentationContest [61]. First, theone-to-one (o2o)matchscore is computed fora
regionpairbasedontheevaluator’sacceptance threshold. Inourexperiments,weused90%as the
acceptance threshold. LetNbethecountofgroundtruthelements, andMthecountof resultelements.
Withtheo2oscore, threemetricsarecalculated: detectionrate (DR), recognitionaccuracy(RA),and
performancemetric (FM).
4.3. IsolatedCharacter/GlyphRecognition
4.3.1.Datasets
Thepalmleafmanuscriptdatasets for isolatedcharacter/glyphrecognitiontaskarepresented
inTable3. For theBalinesecharacterdataset,Balinesephilologistsmanuallyannotatedthesegment
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zurück zum
Buch Document Image Processing"
Document Image Processing
- Titel
- Document Image Processing
- Autoren
- Ergina Kavallieratou
- Laurence Likforman-Sulem
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2018
- Sprache
- deutsch
- Lizenz
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03897-106-1
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 216
- Schlagwörter
- document image processing, preprocessing, binarizationl, text-line segmentation, handwriting recognition, indic/arabic/asian script, OCR, Video OCR, word spotting, retrieval, document datasets, performance evaluation, document annotation tools
- Kategorie
- Informatik