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• W2895683890 abstract "In this paper, we present a sub-word recognition method for historical Arabic manuscripts, using convolutional neural networks. We investigate the benefit of extending training set with synthetically created samples in comparison to augmentation. We show that annotating around ten pages of a manuscript and extending it, is sufficient for successful sub-word recognition in the whole manuscript. In addition, we show the contribution of using different combinations of training sets and compare their sub-word recognition performance in the whole manuscript." @default.
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• W2895683890 date "2018-03-01" @default.
• W2895683890 modified "2023-09-26" @default.
• W2895683890 title "Synthesizing versus Augmentation for Arabic Word Recognition with Convolutional Neural Networks" @default.
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• W2895683890 doi "https://doi.org/10.1109/asar.2018.8480189" @default.
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