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Document Image Processing
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J. Imaging 2018,4, 41 optimizeroutperformedtheSGDandAdagradoptimizers. TheAdaDelta,AdaMax,andRMSProp optimizersoutperformedtheAdamoptimizer. Figure5showstheperformanceof individualoptimizer. Table 5. In this table,we report the results in termofmaximum,minimum,mean, and standard deviation recognition accuracy obtained withNA-6 on ISIDCHARwhen the system trained for 50epochswith thedifferentoptimizers. Thebest scoresare inbold. RecognitionAccuracy DifferentOptimizers SGD Adagrad Adam AdaDelta AdaMax RMSProp Maximum 0.931 0.9364 0.9558 0.9565 0.9579 0.9602 Minimum 0.6933 0.7703 0.7585 0.7605 0.7851 0.8067 Mean 0.9168 0.9280 0.9411 0.9457 0.9448 0.9463 Std.Deviation 0.0365 0.0252 0.0274 0.0311 0.0256 0.0252 6*' (SRFK $GDJUDG (SRFK $GDP (SRFK $GD'HOWD (SRFK $GD0D[ (SRFK 5063URS (SRFK (a) (b) (c) (d) (e) (f) Figure5. In thisfigure,wedrawtherecognitionaccuracyobtainedwithNA-6networkarchitecture at different optimizers (a) SGD; (b)Adagrad; (c)Adam; (d)AdaDelta; (e)AdaMax; (f) RMSProp; onthe ISIDCHARdatabaseateachepoch. 97
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Document Image Processing
Title
Document Image Processing
Authors
Ergina Kavallieratou
Laurence Likforman-Sulem
Editor
MDPI
Location
Basel
Date
2018
Language
German
License
CC BY-NC-ND 4.0
ISBN
978-3-03897-106-1
Size
17.0 x 24.4 cm
Pages
216
Keywords
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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Informatik
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Document Image Processing