A method for object recognition that is invariant under translation, rotation and scaling is addressed. The first step of the method (pre-processing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a holographic nearest-neighbor (HHN) algorithm, in which vectors obtained in the pre-processing step are used as inputs to it. The algorithm is tested in character recognition, using the 26 upper-case letters of the alphabet. Only four different orientations and one size (for each letter) were used for training. Recognition was tested with 17 different sizes and 14 rotations. The results are encouraging, since we achieved 98% correct recognition. Tolerance to boundary deformations and random noise was tested. Results for character recognition in "real" images of car plates are presented as well.
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