Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/6680
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dc.contributor.authorQaroush, Azizen_US
dc.contributor.authorAwad, Abdalkarimen_US
dc.contributor.authorModallal, Mohammaden_US
dc.contributor.authorZiq, Maliken_US
dc.date.accessioned2021-03-09T08:56:55Z-
dc.date.available2021-03-09T08:56:55Z-
dc.date.issued2020-10-10-
dc.identifier.urihttp://hdl.handle.net/20.500.11889/6680-
dc.description.abstractOptical Character Recognition OCR is an essential part of many real-world applications such as digital archiving, automatic number plate recognition, handle cheques, etc. However, developing an OCR for printed Arabic text is still a challenging and open research field due to the special characteristics of Arabic cursive script. In this paper, we propose a segmentation-based, omnifont, open-vocabulary OCR for printed Arabic text. The proposed approach doesn’t require an explicit font type recognition stage. It uses an explicit, indirect character segmentation method. The presented segmentation method is baseline dependent and employs a hybrid, three-steps character segmentation algorithm to handle the problem of character overlapping. Besides, it uses a set of topological features that are designed and generalized to make the segmentation approach font independent. The segmented characters are fed as an input to a convolutional neural network for feature extraction and recognition. The APTID-MF data set has been used for testing and evaluation. The average accuracy of the proposed segmentation stage is 95%, while the average accuracy of the recognition stage is 99.97%. The whole approach achieves an average accuracy of 95% without using font-type recognition or any post-processing techniques.en_US
dc.subjectOptical character recognitionsen_US
dc.subjectArabic character sets (Data processing)en_US
dc.subjectMono-fonten_US
dc.subjectMixed-fonten_US
dc.subjectPattern recognition systemsen_US
dc.subjectImage segmentationen_US
dc.subjectCharacter segmentationen_US
dc.subjectImage processing - Digital techniquesen_US
dc.subjectConvolutional neural networksen_US
dc.titleSegmentation-based, omnifont printed Arabic character recognition without font identificationen_US
dcterms.identifierhttps://doi.org/10.1016/j.jksuci.2020.10.001en_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectnoneen_US
dc.identifier.doihttps://doi.org/10.1016/j.jksuci.2020.10.001-
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:6. BZU Dataset Collection
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