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Preprocessing and Binarization of Inscription Images using Phase Based Features

机译:使用基于阶段的特征对铭文图像进行预处理和二值化

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Epigraphs are important sources for reshaping our culture and history. They have a remarkable importance to mankind. But modern epigraphists find it difficult to interpret the information in scripts. It is mainly because inscriptions are eroded over a period of time due to natural calamities. Scripts of ancient times are largely unknown. Character sets used have changed from one form to another over the centuries. Therefore, for reading ancient scripts the characters have to be extracted. In this paper, a model for enhancement and binarization of historical epigraphs is proposed. This model consists phase congruency and of Gaussian model based background elimination using expectation maximization(EM) algorithm, preprocessing and binarization. In binarization, phase based features are used with specialised filters. Adaptive Gaussian filters are used to smoothen the output images. Weighted mean angle is calculated to differentiate the foreground from the background. EM algorithm removes the background noise completely where foreground characters are untouched. Proposed method is tested on different datasets of inscriptions and epigraphs. Obtained results are compared with the existing classical algorithms.
机译:题词是重塑我们的文化和历史的重要来源。它们对人类具有非同寻常的重要性。但是现代的碑文学家发现很难用文字来解释这些信息。主要是因为自然灾害使铭文在一段时间内受到侵蚀。古代的文字在很大程度上是未知的。几个世纪以来,使用的字符集已从一种形式更改为另一种形式。因此,为了阅读古代文字,必须提取字符。本文提出了一种对历史题词进行增强和二值化的模型。该模型包括相位一致性和基于高斯模型的背景消除,其中使用了期望最大化(EM)算法,预处理和二值化。在二值化中,基于相位的特征与专用滤波器一起使用。自适应高斯滤波器用于平滑输出图像。计算加权平均角度以区分前景与背景。 EM算法可完全消除未触及前景字符的背景噪声。建议的方法在铭文和碑文的不同数据集上进行了测试。将获得的结果与现有的经典算法进行比较。

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