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- W2894928725 abstract "We present a Fully Convolutional Network based method for layout analysis of non-binarized historical Arabic manuscripts. The document image is segmented into main text and side text regions by dense pixel prediction. Convolutional part of the network can learn useful features from the non-binarized document images and is robust to degradation and uncontrained layouts. We have evaluated the proposed method on a private dataset containing challenging historical Arabic manuscripts to demonstrate it effectiveness." @default.
- W2894928725 created "2018-10-12" @default.
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- W2894928725 date "2018-03-01" @default.
- W2894928725 modified "2023-09-24" @default.
- W2894928725 title "Binarization Free Layout Analysis for Arabic Historical Documents Using Fully Convolutional Networks" @default.
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- W2894928725 doi "https://doi.org/10.1109/asar.2018.8480333" @default.
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