基于视觉显著性与边缘密集度的文本区域定位

Text Localization Algorithm Based on Visual Saliency and Edge Density

为解决传统算法对文本区域检测查准率较低的问题,从自然场景文本特性出发,提出了一种基于视觉显著性与边缘密集度的鲁棒性文本定位方法。首先利用谱残差理论提取图像的显著性区域,然后在提取的显著性区域中寻找边缘密集度大的区域,以此构建候选连通域,利用少量的先验信息滤除其中的非文本区域。在标准数据集上的实验结果表明,与单纯利用边缘特征进行文本区域检测的方法相比,该方法可获得70%的综合检测率。

In order to solve the problem of low precision in traditional text localization algorithm,a robust text localization method based on visual saliency and edge density is proposed from the perspective of natural scene text properties. Firstly,spectral residual theory is used to extract the saliency region of the image,then search for the regions of large edge density in the saliency regions and construct candidate connected regions,using a small amount of prior information to filter out non text area. The experimental results on the standard data set show that this method can obtain 70% comprehensive detection rate compared to the method of text region detection using edge feature.