Abstract
This paper presents a novel algorithm, called Radial Sector Coding (RSC), for Translation, Rotation and Scale invariant character recognition. Translation invariance is obtained using Center of Mass (CoM). Scaling invariance is achieved by normalizing the features of characters. To obtain most challenging rotation invariance, RSC searches a rotation invariant Line of Reference (LoR) by exploiting the symmetry property for symmetric characters and Axis of Reference (AoR) for nonsymmetric characters. RSC uses the LoR to generate invariant topological features for different characters. The topological features are then used as inputs for a multilayer feed-forward artificial neural network (ANN). We test the proposed approach on two widely used English fonts Arial and Tahoma and got 98.6% recognition performance on average.
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