Timothy W. Jacobs

Segmentation for mixed raster contents with multiple extracted constant color areas

Mixed Raster Contents (MRC) is a powerful image representation concept in achieving high compression ratios while maintaining high reconstructed image quality. The Multiple Extracted Constant Color Areas (MECCA) model has the advantages of its ease of decomposition and inherent text enhancement and noise reduction features. This paper presents a segmentation algorithm that extracts uniform text and other uniform color objects that carry detail information. The algorithm consists of four steps. First the text and objects are extracted from the image. Next, they are tested for color constancy and other features to decide if they should be represented by the MRC foreground layers. The objects that are chosen are then clustered in color space. The page is finally segmented such that each foreground layer codes the objects from the same color cluster.