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A New Wavelet-Median-Moment based Method for Multi- Oriented Video Text Detection

机译:一种新的基于小波中位数的多型视频文本检测方法

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In this paper, we present a new method based on wavelet-median-moments and a novel idea of angle projection for detecting multi-oriented text in video. The proposed method uses wavelet decomposition first to obtain three high frequency sub-bands (LH, HL and HH) and then median moments are computed on the average sub-bands of the three high frequency sub-bands to brighten the text pixels. K-means clustering (K=2) is used for obtaining text pixels from the wavelet-median-moments features (WMMF). Text candidates are obtained by mapping the output of K-means on Sobel edge map of the original input frame. To deal with multi-oriented text, we introduce a new idea of Angle Projection (AP) based on boundary growing and nearest neighbor concepts from the text candidates instead of conventional projection profiles. The proposed method is experimented on horizontal text data, non-horizontal text data, temporal data, nontext data and camera based images (scene text data of ICDAR 2003 competition) to show that the proposed method is superior to existing methods.
机译:在本文中,我们介绍了一种基于小波中值的新方法和用于检测视频中多向文本的角度投影的新方法。所提出的方法首先使用小波分解以获得三个高频子带(LH,HL和HH),然后在三个高频子带的平均子带上计算中值矩,以使文本像素提升。 K-means群集(k = 2)用于从小波中间矩特征(WMMF)中获取文本像素。通过在原始输入框的Sobel边缘映射上映射K-means的输出来获得文本候选。要处理多面向多种文本,我们将基于来自文本候选的边界生长和最近的邻概念而不是传统投影配置文件来介绍角度投影(AP)的新思路。所提出的方法在水平文本数据,非水平文本数据,时间数据,非文本数据和基于摄像机的图像上进行了实验(ICDAR 2003竞争的场景文本数据),以表明所提出的方法优于现有方法。

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