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Diacritical Language OCR Based on Neural Network: Case of Amazigh Language

机译:基于神经网络的变音OCR:以Amazigh语言为例

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Document paper conversion into electronic format has become indispensable task in many areas, especially for digitizing and translating printed texts. In this context, several approaches have been studied focusing mainly on character recognition for diacritic-free languages. However in this paper, we are interested in the Amazigh language transcribed in Latin, distinguished by its diacritical characters. Thus, we propose to use a system based on neural networks, and to study its behavior against this type of characters.
机译:在许多领域,尤其是对于数字化和翻译印刷文本而言,将文档纸转换为电子格式已成为必不可少的任务。在这种情况下,已经研究了几种主要针对无音调语言的字符识别的方法。但是,在本文中,我们对以拉丁字母转录的Amazigh语言感兴趣,该语言以其变音字符为特征。因此,我们建议使用基于神经网络的系统,并研究其针对此类字符的行为。

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