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J. Imaging 2018,4, 32 recognition protocols in terms ofCRR,WRRandLRRmetrics. The SID-OCR systemhas shown superiority inallprotocols. ThebestaccuraciesareachievedontheTunisiaNat1channel subset (p6.3) with0.94asaCRRand0.62asaLRR.TheIWATAsystemperformswell forallSDprotocolsespecially for theCRR/WRRmetrics.However itscurrentversionis incompatiblewithHDresolution. Theresult showsthatoursystemhas lowrecognitionratewhenfacingdifferent textpatternsandresolutions, i.e.,globalProtocol9. Basedonourknowledgeabout theshapesofArabiccharacters,wedivide the causesoferrors into twoclasses: charactersimilarityandinsufficientsamplesofpunctuation,digits and symbols. Severalmeasures canbe taken tominimize the character error rate, for instanceby integrating languagemodelsordropoutmechanism. Table7.Performanceof textdetectionsystemsevaluatedonthe test setofAcTiV-D. Protocol System Precision Recall Fmeasure 1 LADI[46] 0.86 0.84 0.85 SysA[14] 0.77 0.76 0.76 Gaddo[52] 0.52 0.49 0.51 4.1 LADI[46] 0.74 0.76 0.75 SysA[14] 0.69 0.6 0.64 Gaddo[52] 0.47 0.61 0.54 4.2 LADI[46] 0.8 0.75 0.77 SysA[14] 0.66 0.55 0.6 Gaddo[52] 0.41 0.5 0.45 4.3 LADI[46] 0.85 0.82 0.83 SysA[14] 0.68 0.71 0.69 Gaddo[52] 0.34 0.49 0.41 4.4 LADI[46] 0.71 0.76 0.73 SysA[14] 0.5 0.49 0.49 Gaddo[52] - - - Table8.Performanceof therecognitionsystemsevaluatedonthe test setofAcTiV-R. Protocol System CRR WRR LRR 3 SIDOCR[51] 0.90 0.71 0.51IWATA[53] - - - 6.1 SIDOCR[51] 0.89 0.70 0.51IWATA[53] 0.88 0.67 0.46 6.2 SIDOCR[51] 0.94 0.68 0.41IWATA[53] 0.9 0.68 0.39 6.3 SIDOCR[51] 0.94 0.81 0.62IWATA[53] 0.94 0.77 0.56 6.4 SIDOCR[51] 0.93 0.73 0.52IWATA[53] 0.9 0.73 0.48 9 SIDOCR[51] 0.73 0.58 0.32IWATA[53] - - - 5.3.2. TrainingwithAcTiV2.0 Toexamine theeffectof increasing thenumberof trainingsamplesontheaccuracyofour text detector, we conduct the same experiment of Protocol 6.1, in Table 7, using training-set2, which includes roughly thedoubleof samples (600 frames) than training-set1 (seeTable4). Weobserved that thedetectionratesofour textdetectorhavebeen increasedasexpected. Specifically, the recall increasesby2%andtheprecisionincreasesby5%.Thiscanbeexplainedbythe increase inthenumber 202
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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
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Austria-Forum
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Document Image Processing