Low-complexity comprehensive labeling and enhancement algorithm for compound documents

We present a multiresolutional algorithm that segments a compound document and uses the results of the segmentation for document enhancement in copier applications. The document is initially segmented into halftone and non-halftone areas. Based on this segmentation the location of the edges due to text, graphics, and images (and not due to halftone dots) are detected on halftone as well as on non-halftone portions. We further detect constant-tone regions within non-halftone areas for subsequent bleed-through removal applications. Edge enhancement on detected edges and descreening on detected halftones are carried out. The algorithm can detect general halftones over regions of arbitrary sizes and shapes, and it can be straightforwardly adjusted for operation at various dpi resolutions. We obtain high detection probabilities on compound multilingual documents containing halftones and fine text. The proposed enhancement stage is tolerant of segmentation errors providing robust performance for the remaining problem cases. Our main contribution is the accomplishment of these tasks with a single pass algorithm that is computationally very simple and that requires less than 1% of full page memory, with active memory requirements less than .02% of full page memory. The operation of the algorithm can be imagined as a very thin line (of thickness the size of a “full-stop” in 11pt text) that rapidly scans an input page while simultaneously producing an output page.