A Statistical Method for an Automatic Detection of Form Types

In this paper, we present a method to classify forms by a statistical approach; the physical structure may vary from one writer to another. An automatic form segmentation is performed to extract the physical structure which is described by the main rectangular block set. During the form learning phase, a block matching is made inside each class; the number of occurrences of each block is counted, and statistical block attributes are computed. During the phase of identification, we solve the block instability by introducing a block penalty coefficient, which modifies the classical expression of Mahalanobis distance. A block penalty coefficient depends on the block occurrence probability. Experimental results, using the different form types, are given.