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J. Imaging 2018,4, 39 Table15.ClassificationresultsusingLogisticRegressionsecondaryclassifier. Class Class A B C D E F G H I J K L A 583 0 1 2 0 0 5 4 2 0 1 2 B 0 598 0 2 0 0 0 0 0 0 0 0 C 1 0 598 0 0 0 0 0 1 0 0 0 D 1 2 0 594 0 0 0 1 0 0 0 2 E 1 0 0 0 595 3 0 0 0 0 0 1 F 0 0 0 0 4 589 0 5 1 0 0 1 G 4 0 3 2 0 0 578 2 3 0 8 0 H 3 0 0 0 0 6 1 586 1 0 1 2 I 6 0 1 0 0 0 2 3 587 0 1 0 J 0 0 0 0 0 2 0 1 0 596 0 1 K 1 0 0 0 0 0 5 0 0 0 594 0 L 1 0 1 0 2 0 2 1 0 0 0 593 Table16.ClassificationresultsusingMLPsecondaryclassifier. Class Class A B C D E F G H I J K L A 575 1 2 4 0 0 4 4 4 0 2 4 B 1 597 0 2 0 0 0 0 0 0 0 0 C 1 0 598 0 0 0 0 0 1 0 0 0 D 3 2 0 594 0 0 0 1 0 0 0 0 E 0 0 0 0 599 1 0 0 0 0 0 0 F 0 0 0 0 2 594 0 3 0 0 0 1 G 7 1 2 1 3 0 577 0 2 1 6 0 H 4 0 0 0 1 7 1 583 1 1 0 2 I 7 0 2 1 0 0 3 0 586 0 1 0 J 0 0 1 0 0 0 0 0 0 599 0 0 K 0 0 0 0 0 0 7 0 0 0 593 0 L 1 0 0 0 4 0 1 0 0 0 0 594 Table17.ClassificationresultsusingRandomForest secondaryclassifier. Class Class A B C D E F G H I J K L A 581 0 3 2 0 0 8 1 4 0 0 1 B 0 597 0 2 0 0 1 0 0 0 0 0 C 1 0 595 0 0 0 2 0 1 0 0 1 D 2 2 0 593 0 0 0 0 0 0 0 3 E 0 0 0 0 598 2 0 0 0 0 0 0 F 0 0 0 0 7 585 0 6 0 1 0 1 G 3 0 4 1 0 0 585 1 2 0 4 0 H 4 0 2 0 0 8 0 582 1 0 1 2 I 9 0 1 0 0 0 3 2 584 0 1 0 J 0 0 0 0 0 2 0 2 0 595 0 1 K 0 0 0 0 0 0 6 0 0 0 593 1 L 1 0 4 0 1 0 0 1 0 1 0 592 Script recognition isadifficult taskgiventhevariation in thewords foraparticularscript. But the resultsarereallyencouragingforbuildingamodelthatcanidentifythescriptwithcertainty. Theresults obtainedafter thecombinationexceeds thereportedaccuracies for thiscertain taskandhenceset the newbenchmark. Table18provides theclasswiseaccuracyalongwith theoverallaccuracyachieved byeachprocedureused in thepaper. It showsthat theLogisticRegressionclassifieractingontheMLP classifieroutputsprovide thebest resultwhere98.45%accuracy isobtainedwithan improvement of 7.05%. Results are also obtained from the feature level combination. Natural combination or concatenationof twofeaturesata timeandall three togetheraredone. Thenewfeatureset formedin eachcaseundergoes thesameprocessofclassification throughtheMLPclassifier. Thecomprehensive results forcomparisonaregiven in the followingTable19. 166
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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
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Document Image Processing