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Ligature categorization based Nastaliq Urdu recognition using deep neural networks

机译:基于Ligature分类的NAStaliq URDU识别使用深神经网络

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摘要

The cursive nature, Nastaliq writing style and a large number of different ligatures make ligature recognition very difficult in Urdu. In this paper, we present a segmentation-free approach to holistically recognize Urdu ligatures. We first generate a rich dataset which contains 17,010 ligatures with different orientation and different degrees of noise. Secondly, the ligatures are clustered (categorized) in order to reduce the search space and make the learning robust. Finally, we employ a deep neural network with dropout regularization to classify ligatures. The detailed experiments show that a deep neural network with dropout regularization and clustering of ligatures significantly enhances the classification accuracy.
机译:草坪性质,Nastaliq写作风格和大量不同的韧带使得乌尔都语非常困难。在本文中,我们提出了一个完全识别乌尔都语的分割方法。我们首先生成丰富的数据集,其中包含具有不同方向和不同噪音的17,010个脱韧带。其次,群化是聚类(分类)以减少搜索空间并使学习稳健。最后,我们采用了一个深度神经网络,辍学正规化来分类韧带。详细实验表明,具有丢弃正则化和韧带聚类的深度神经网络显着提高了分类准确性。

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