抄録
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 non-symmetric
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.
