Character Region Segmentation Based On Stroke Stable Regions

Region segmentation is the key procedure in various text related image processing tasks. A good region extractor, which separates text area from complex background clutter, will reduce the burden of subsequent text grouping and post-processing functions. This paper propose a character region segmentation method based on a new concept named Stroke Stable Region (SSR) to achieve a better precision than many off-the- shelf region extractors such as MSER in the text segmentation task. Our work presented in this paper is inspired by the structure of MSER. However, instead of evaluating the area variation of each connected component, we proposed a novel parameter, stroke time, to measure the possibility that a pixel belongs to a character or a stroke by analyzing its character affinity. The experiments show that SSR tends to extract the visual objects with prominent text characteristics and is capable of suppressing various background noise. In a text extraction task on the ICDAR 2003 dataset, the SSR based method reduces the extracted noise components to about 1/3 of those obtained by the MSER based method, maintaining the same level of recall rate. The proposed algorithm was successfully applied to a wide range of text segmentation tasks.