28. The system of claim 22 wherein: each optimum set is represented by a corresponding master row having the set of columns from the corresponding initial subset of rows, each corresponding master row comprising a binary vector with a binary 1 in particular columns in the set of columns for the corresponding initial subset of rows that are elements of the corresponding optimum set and a binary 0 in other particular columns in the set of columns for the corresponding initial subset of rows that are not elements of the corresponding optimum set; and the division module comprises a clustering module using a clustering algorithm to: determine one or more clusters of one or more row points for each initial subset of rows, each row point corresponding to one of the one or more text rows in the corresponding initial subset of rows and comprising at least two members of a group consisting of a row distance, a row matches, and a row length for the one of the one or more text rows or normalized values thereof; determine a cluster closeness value for each cluster in each initial subset of rows, each cluster closeness value based on a row matches average and a row distances average for text rows corresponding to row points of a corresponding cluster or normalized values thereof; determine a final cluster for each initial subset of rows, each final cluster having a smallest cluster closeness value from cluster closeness values of the one or more clusters of the corresponding initial subset of rows, each final cluster comprising at least one of the row points, the at least one of the row points corresponding to the at least one of the one or more text rows of the corresponding initial subset of rows; and split the one or more text rows in each corresponding initial subset of rows into at least the final subset of rows and another subset of rows, the final subset of rows comprising the at least one of the one or more text rows of the corresponding initial subset of rows that corresponds to the at least one of the row points of a corresponding final cluster, the other subset of rows comprising either no text rows or at least one other text row of the corresponding initial subset of rows that does not include the at least one of the one or more text rows.
29. The system of claim 28 wherein: the clustering module determines a final distances vector for each final subset of rows, each final distances vector comprising distances of the at least one of the one or more text rows of the corresponding final subset of rows to the corresponding master row associated with the corresponding final subset of rows, each final distances vector having a final row distances average; and the clustering module determines a final matches vector for each final subset of rows, each final matches vector comprising, for each one of the at least one of the one or more text rows in the corresponding final subset of rows, a sum of matches between a binary 1 in the particular columns of the corresponding master row and another binary 1 in corresponding particular columns of the each one of the at least one of the one or more text rows, each final matches vector having a final row matches average; and the confidence factor comprises a confidence factor ratio with at least one member of a group consisting of: a normalized rows frequency multiplied by the final row matches average of the final matches vector for the corresponding final subset of rows in a numerator and the final row distances average of the final distances vector for the corresponding final subset of rows in a denominator, the normalized rows frequency comprising a first number of the at least one of the one or more text rows in the corresponding final subset of rows divided by a second number of text rows in the at least one document image; and the final row matches average of the final matches vector in the numerator and the final row distances average of the final distances vector in the denominator.