Deep Arabic document layout analysis

I. Amer, Salma Hamdy, M. Mostafa
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引用次数: 9

Abstract

Document layout analysis (DLA) is an essential step for Optical Character Recognition Systems (OCR). The text of the document fed to the OCR must be extracted first and isolated from images if exist. The DLA task is difficult as there is no fixed layout for all documents, but instead, there are several layouts. There are various approaches for DLA for various different languages. In this paper, some of the previous techniques used in this field will be listed and then we will discuss the proposed method that depends on deep learning for documents' text localization. We used Arabic Printed Text Image database (APTI [19]), ImageNet [18] and a dataset collected from different Arabic newspapers for training and evaluation.
深度阿拉伯语文件布局分析
文档布局分析(DLA)是光学字符识别系统(OCR)的关键步骤。必须首先提取提供给OCR的文档的文本,并从存在的图像中分离出来。DLA任务是困难的,因为没有固定的布局为所有的文件,而是有几个布局。针对不同的语言,DLA有不同的方法。在本文中,我们将列出该领域以前使用的一些技术,然后我们将讨论基于深度学习的文档文本本地化方法。我们使用阿拉伯语印刷文本图像数据库(APTI[19])、ImageNet[18]和从不同阿拉伯语报纸收集的数据集进行训练和评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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