自然场景图像中基于视觉显著性的文本区域检测

Visual Saliency-Based Detection of Text Region in Natural Scene Images

从自然场景图像中抽取文本信息有利于场景图像的内容分析.文中根据图像中文本通常在局部区域具有显著性的特点,提出多尺度包围盒视觉显著性模型,并利用该模型设计一种可以融合边缘和纹理信息的候选文本检测方法.首先在Lab颜色空间构造基于边缘和纹理信息的图像同质性,并利用它将图像映射到同质性空间;然后根据多尺度包围盒视觉显著性模型求Lab颜色空间的同质性均值图像;最后求同质映射图像与同质性均值图像的加权欧氏距离,将其作为显著性度量,以提取文本区域.自然场景图像的实验表明:与单纯利用边缘检测或同质性映射进行文本检测的方法相比,文中提出的方法能够更好地抑制背景的干扰,这有利于进一步将文本区域与背景剥离,进行更精确的文本定位.

Extracting text information from images captured in natural scenes is helpful for the content analysis of images. In this paper, according to the fact that the texts in images is often salient in local regions, a novel visual saliency model with multi-scale bounding box is proposed, based on which a new method combining the edge and texture information is designed for the candidate text detection. In this method, first, Lab color space is used to construct the edge and textural information-based image homogeneity, and by using this characteristic, the image is mapped into the homogeneity domain. Then, the proposed model is employed to generate average homogeneity ima- ges. Finally, the weighted Euclidean distance between the homogeneity image and the average homogeneity image is determined, and is taken as the saliency measure to extract text regions. Experimental results of natural scene images show that, as compared with the text detection methods based on the edge or the homogeneity, the proposed method can better restrain the background noise, which helps to further segment the text regions from the back- ground and achieve more accurate text location.