1. A computer-readable medium encoded with a document processing system for processing at least one document image comprising a plurality of text rows and a plurality of characters, each text row having at least one character, the document processing system comprising a plurality of modules executable by at least one processor, the modules comprising: an image labeling system configured to label the characters in the document image to determine a size of the characters and to determine at least one morphological structuring element based on the size of the characters; a character block creator configured to: create a plurality of character blocks from the characters in the document image by performing a morphological closing on the document image using the at least one structuring element, each text row having at least one character block; and label each character block to determine at least one spatial position of at least one alignment for each character block in each text row, the at least one alignment comprising at least one member of a group consisting of a left alignment and a right alignment, the left alignment comprising the at least one spatial position for a left side of each character block, the right alignment comprising the at least one spatial position for a right side of each character block; and a classification system comprising: a subsets module configured to: determine a column for the at least one alignment of each character block in each text row, each text row having a physical structure defined by at least one column of the at least one alignment of the at least one character block in that text row; and determine an initial subset of rows for each column having more than one character block aligned in that column in the text rows, each initial subset of rows comprising one or more text rows having the at least one alignment of the at least one character block in a selected column, each initial subset of rows having a set of columns comprising the selected column and other columns in the one or more text rows; an optimum set module configured to determine an optimum set and a master row for each initial subset of rows, each optimum set comprising a most representative set of columns selected from the set of columns of a corresponding initial subset of rows, each master row comprising a binary 1 in particular columns of a corresponding optimum set for the corresponding initial subset of rows and a binary 0 in other particular columns in the set of columns for the corresponding initial subset of rows; a thresholding module configured to: determine an initial distances vector for each initial subset of rows, each initial distances vector comprising a distance between each of the one or more text rows in the corresponding initial subset of rows and a corresponding master row for the corresponding initial subset of rows; determine an initial distances vector threshold for each initial distances vector using a thresholding algorithm; determine a final distances vector for each initial distances vector, each final distances vector comprising one or more of the distances between the one or more text rows in the corresponding initial subset of rows and the corresponding master row, each of the one or more distances being under a corresponding initial distances vector threshold for a corresponding initial distances vector; determine a final subset of rows for each initial subset of rows, each final subset of rows comprising at least some of the one or more text rows of the corresponding initial subset of rows that have the one or more distances in a corresponding final distances vector under the corresponding initial distances threshold; determine a mean of distances for each final distances vector; determine a variance for each final subset of rows, each variance between the at least some text rows in the corresponding final subset of rows and the corresponding master row for the corresponding final subsets of rows; determine a frequency of rows for each final subset of rows; determine a confidence factor for each final subset of rows, each confidence factor measuring a similarity of the physical structures of each one of the at least some text rows in the corresponding final subset of rows to each other one of the at least some text rows in the corresponding final subset of rows, the confidence factor comprising the mean, the variance, and the frequency; and determine a best confidence factor for each particular text row in the document image, each particular text row having one or more confidence factors corresponding to one or more final subsets of rows in which the particular text row is an element; and a classifier module configured to create one or more classes of text rows, each class comprising one or more particular text rows having a same best confidence factor.