1. A computer-implemented method for extraction of features from document images containing content noise and structural noise comprising: receiving a dataset of text sequences extracted from noisy document images that include content noise comprising OCR errors and structural noise comprising segmentation errors; automatically mining the input dataset of text sequences in an unsupervised mode to identify a set of contiguous, frequent patterns, some of the frequent patterns comprising factual patterns consisting of fragments of others of the frequent patterns, wherein each identified factual pattern represents a part or all of a text sequence which is not generalized so as to read on a plurality of different fragments, and in the text sequences, the mining being performed without consideration of class labels applied to the text sequences; filtering the extracted patterns to generate a set of features, the filtering comprising at least one of: filtering out redundant patterns, and filtering out irrelevant patterns; fusing at least some of the features in the set of features to generate a reduced set of features, the fusing of features in the set of features including identifying a pair of complementary features and merging the complementary features, the identifying of complementary features including determining a symmetrical uncertainty for each of the features and their class label and a symmetrical uncertainty for their disjunction and the class label; and outputting at least some of the features in the reduced set of features.