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A novel method for binarization of badly illuminated document images

机译:一种不良光照文档图像二值化的新方法

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This paper presents a novel document image binarization technique that separates text from background in badly illuminated document images. This technique is based on background estimation by using morphological closing operation. Binarization methods can be categorized into two categories: the global methods which do not work well in badly illuminated images, and the local methods which are usually parametric. In this paper a new local method is proposed. The most important feature of this local method is that, contrary to other common local methods, it is not dependent on any parameter. In other words it is an automatic method. Morphological closing operation is used to compensate for uneven background illumination. Closing operation is applied to remove small dark details while living the overall gray and larger dark features relatively undisturbed. Experiment results show that the proposed method offers better result for document images with bad degradation and lighting variance in comparison to former common methods.
机译:本文提出了一种新颖的文档图像二值化技术,该技术将照明不良的文档图像中的文本与背景分离开来。该技术基于使用形态学闭合运算的背景估计。二值化方法可以分为两类:在光照不良的图像中效果不佳的全局方法,以及通常是参数化的局部方法。本文提出了一种新的局部方法。此本地方法的最重要特征是,与其他常见的本地方法相反,它不依赖于任何参数。换句话说,这是一种自动方法。形态关闭操作用于补偿不均匀的背景照明。关闭操作适用于删除较小的暗部细节,同时保留相对不受干扰的整体灰色和较大的暗部特征。实验结果表明,与以前的常用方法相比,该方法对具有较差劣化和光照变化的文档图像提供了更好的效果。

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