Text segmentation from web images using two-level variance maps

Variance map can be used to detect and distinguish texts from the background in images. However previous variance maps work as one level and they revealed a limitation in dealing with diverse size, slant, orientation, translation and color of texts. In particular, they have difficulties in locating texts of large size or texts with severe color gradation due to specific value in mask sizes. We present a method of robustly segmenting text regions in complex web color images using two-level variance maps. The two-level variance maps works hierarchically. The first level finds the approximate locations of text regions using global horizontal and vertical color variances with the specific mask sizes. Then the second level segments each text region using intensity variation with a local new mask size, in which a local new mask size is determined adaptively. By the second process, backgrounds tend to disappear in each region and segmentation can be accurate. Highly promising experimental results have been obtained using the our method in 400 web images.