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Detecting characters in grey-scale scene images

机译:检测灰度场景图像中的字符

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This paper proposes a method for detecting characters in grey-scale scene images for navigating vision-based mobile robot by character information. First, we extract subregions with high spatial frequency and great variance in grey-level from an input scene image as candidates of character components. Then, we select characters by using several heuristic such as constraints of size and shape, bimodality of an intensity histogram, alignment and proximity of characters. We conducted an experiment using 20 indoor and 40 outdoor images. As a result, character lines are detected with a high rate of 80
机译:提出了一种基于灰度信息的灰度场景图像中字符检测的方法。首先,我们从输入场景图像中提取出具有高空间频率和灰度方差很大的子区域作为字符成分的候选者。然后,我们通过使用几种启发式方法来选择字符,例如大小和形状的约束,强度直方图的双峰性,字符的对齐和接近度。我们使用20个室内和40个室外图像进行了实验。结果,以80的高比率检测到字符行

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