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Text detection in color scene images based on unsupervised clustering of multi-channel wavelet features

机译:基于多通道小波特征无监督聚类的彩色场景图像文本检测

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Texts in natural scenes provide us with much useful information. In order to use such information automatically, it is necessary to make computers detect text regions in the images. Gllavata et. al. proposed a method based on unsupervised classification of high frequency wavelet coefficients for text detection in video frames [Gllavata et. al. (2004)]. Although the method is very accurate, it does not work so well with some color images, since it lacks the ability of discriminating color difference. This paper proposes an enhanced version of the method. We develop a new unsupervised clustering technique for the classification of multi-channel wavelet features to deal with color images. Experimental results show that the new method yields better results for color scene images.
机译:自然场景中的文字为我们提供了许多有用的信息。为了自动使用这些信息,有必要使计算机检测图像中的文本区域。 Gllavata等。等提出了一种基于高频子波系数无监督分类的视频帧文本检测方法[Gllavata等。等(2004)]。尽管该方法非常准确,但由于缺乏区分色差的能力,因此对于某些彩色图像效果不佳。本文提出了该方法的增强版本。我们开发了一种新的无监督聚类技术,用于对多通道小波特征进行分类以处理彩色图像。实验结果表明,该新方法对彩色场景图像效果更好。

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