Translation, Rotation and Scale Invariant 2-D Object Recognition Abstract A method for object recognition invariant under translation, rotation and scaling is addressed. The first step of the method (preprocessing) 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 algorithm (HNN), where vectors obtained in the preprocessing 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 manual and random noise was tested. Results for character recognition in real images of car plates are also presented. Keywords - Invariant object recognition, character recognition, holographic nearest neighbor. @inproceedings{Torres95, author = "Luz Abril Torres M.", address = "Cuernavaca, Mor., Mexico", year = "1995", month = "September", booktitle = "XII National Reunion of Artificial Intelligence (RNIA)", title = "Translation, Rotation and Scale Invariant Object Recognition", } @MastersThesis{, author = "Luz Abril Torres M.", title = "Translation, Rotation and Scale Invariant 2-D Object Recognition", school = "Instituto Tecnol\'ogico y de Estudios Superiores de Monterrey, Campus-Morelos", year = "1995", address = "Cuernavaca, Mor., Mexico", month = "Ago", }