University of Bahrain
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Towards Author Recognition of Ancient Arabic Manuscripts Using Deep Learning: A Transfer Learning Approach

Show simple item record Khayyat, Manal M. Elrefaei, Lamiaa A. 2020-07-14T20:03:48Z 2020-07-14T20:03:48Z 2020-09-01
dc.identifier.issn 2210-142X
dc.description.abstract Due to the significance of ancient Arabic manuscripts and their role in enriching valuable historical information, this study aims to collect Arabic manuscripts in a dataset and classify its images to predict their authors. We accomplished this study through two main phases. First is the data collection phase. Arabic manuscripts gathered, including 52 Arabic Authors. Second is the models’ development phase to extract the visual features from the images and train the networks on them. We built four deep learning models named: MobileNetV1, DenseNet201, ResNet50, and VGG19. We configured the models by tuning their learning hyperparameters toward optimizing their recognition process. Afterward, we performed a comparative analysis between all the models to measure their performance. Eventually, we reached that minimizing the learning rate, combining “Sigmoid” with “Softmax”, and increasing the number of neurons on the final classification dense layer improved the networks’ recognition performance significantly since all utilized deep learning models reached above 95% validation accuracy. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject Ancient Arabic Manuscripts en_US
dc.subject Authors Recognition en_US
dc.subject Convolutional Neural Networks en_US
dc.subject Deep Learning Models en_US
dc.subject Learning Hyper-parameters en_US
dc.title Towards Author Recognition of Ancient Arabic Manuscripts Using Deep Learning: A Transfer Learning Approach en_US
dc.type Article en_US
dc.volume 9 en_US
dc.issue 5
dc.pagestart 1 en_US
dc.pageend 18 en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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