1. A computer-implemented method for recognizing patterns in a digital image through document image decomposition, comprising: maintaining a set of exemplary patterns comprised in digital 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 matching each zone hypothesis in the optimal set against the exemplary patterns and identifying the zone hypotheses in the digital image closest matching.
1. A computer-implemented method for recognizing patterns in a digital image through document image decomposition, comprising: maintaining a set of exemplary patterns comprised in digital 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 matching each zone hypothesis in the optimal set against the exemplary patterns and identifying the zone hypotheses in the digital image closest matching. 2. A method according to claim 1 , further comprising: detecting each of the words in the layout layer through Harr-like filters selected from the group comprising horizontal step edge, vertical step edge, horizontal bar, vertical bar, and diagonal block filters, which can each be varied in size and scale.