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Blind recognition of mixed and disturbed correlated ancient texts

机译:盲目识别混合和混乱的相关古代文字

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Separation and recognition of ancient documents and texts that have been mixed and disturbed over centuries, is an interesting problem in image processing area and it has been investigated by many researchers. In recent years, independent component analysis (ICA) method has been used for solving this problem, but independence of sources is an essential assumption in ICA, whereas in some problems, sources are not independent. So in this paper, we have tried to propose a method for separation and recognition of mixed and disturbed correlated ancient documents and texts utilizing MUltiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) algorithms from blind source separation (BSS) techniques. The good performance of this method has been investigated for real images.
机译:在图像处理领域中,分离和识别混合和困扰了数百年的古代文献和文本是图像处理领域中一个有趣的问题,许多研究者对此进行了研究。近年来,使用独立成分分析(ICA)方法来解决此问题,但是源独立​​性是ICA中的基本假设,而在某些问题中,源并不独立。因此,在本文中,我们尝试提出一种利用多信号分类(MUSIC)和通过旋转不变技术(ESPRIT)算法从盲源分离(ESPRIT)算法估计信号参数来分离和识别混合和受干扰的相关古代文献和文本的方法。 BSS)技术。已经针对真实图像研究了该方法的良好性能。

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