Neural network architectures for rotated character recognition

Explores a neural network (NN) approach that is analogous to the human straightforward pattern matching, where some rotation is taking place in high level neurons close to symbols. The main objective is to develop ideas to simulate the rotation and verify them by using a large number of handwritten characters. The author proposes a feedforward NN where the links between input and hidden units are locally connected and weights are symmetrically shared. In the recognition process the total input values to hidden units are rotated according to the number of possible orientations and the activation values of output units are calculated for each orientation to find the best output