2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) 2017
DOI: 10.1109/intelcis.2017.8260051
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Deep Arabic document layout analysis

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Cited by 8 publications
(10 citation statements)
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“…Furthermore, the remarked image classifier CNN [40] (convolutional neural network) is also used in recent studies, where Amer et al [11] proposed two CNN classifiers based on patches and zones for segmenting the text and non-text regions in newspapers. Magazines and newspaper pages are considered the most complex structures that can exist due to the dissimilarity, variety, and comprehensiveness that cannot be found in other types.…”
Section: Arabic Document Analysis Methodsmentioning
confidence: 99%
“…Furthermore, the remarked image classifier CNN [40] (convolutional neural network) is also used in recent studies, where Amer et al [11] proposed two CNN classifiers based on patches and zones for segmenting the text and non-text regions in newspapers. Magazines and newspaper pages are considered the most complex structures that can exist due to the dissimilarity, variety, and comprehensiveness that cannot be found in other types.…”
Section: Arabic Document Analysis Methodsmentioning
confidence: 99%
“…In [6] we proposed a method for Arabic document layout analysis for text localization and separation from images in Arabic newspapers. The method mainly uses a deep convolutional neural network (D-CNN) to classify zones/patches of the document image as text or non-text then the text lines and words of textual zones are extracted.…”
Section: Proposed Methodsmentioning
confidence: 99%
“…Amer ve dig. [11] Arapça gazeteler ve Arapça basılı metinler için CNN tabanlı bir belge düzeni analiz sistemi önerdi. Metin ve metin olmayan bölgeleri bulmada yaklaşık % 90 dogruluk elde ettiler.…”
Section: Iii̇lgi̇li̇ çAlişmalarunclassified