12. A method according to claim 8 , further comprising: determining a prior model for zone relations in each zone model relating to a shape and spatial distribution of the zone hypotheses within the stored digital image characterized by a Gestalt ensemble.
8. A computer implemented method for classifying a digital image through document image decomposition, comprising: storing identifying characteristics representing documents selected from the group comprising forms, preprinted documents, textual documents, and hybrid data comprising one or more of pictorial, graphical, and textual data; extracting generic visual features from a stored digital image; grouping the generic visual features into a primitive layer comprising a plurality of word-graphs that each comprise one of words and visualized features; grouping the one or more of the words into a layout layer comprising a plurality of zone hypotheses that each comprise one or more of the words; expressing causal dependencies between the word-graphs in the primitive layer and the zone hypotheses in the layout layer through zone models that each comprise a joint probability defining a pair of probabilistic models generated through a learned binary edge classifier; expressing each pair of probabilistic models as an optimal set selection problem comprising a set of cost functions and constraints; evaluating the optimal set selection problem through a heuristic search of the cost functions and constraints and providing a non-overlapping optimal set of the zone hypotheses that characterize the stored digital image; and comparing each zone hypothesis in the optimal set to the document identifying characteristics and identifying those documents closest matching.