Character Rotation Absorption Using a Dynamic Neural Network Topology: Comparison With Invariant Features

Abstract. This paper treats on rotation absorption in neural networks for multioriented character recognition. Classical approaches are based on several rotation invariant features. Here, we propose to use a dynamic neural network topology to absorb the rotation phenomenon. The basic idea is to preserve as most as possible the graphical information, that contains all the information. The proposal is to dynamically modify the neural network architecture, in order to take into account the rotation variation of the analysed pattern. We use too a specific topology that carry out a polar transformation inside the network. The interest of such a transformation is to transform the rotation problem from a

• Type: Conference
• Year: 2004
• Where: PRIS
• Authors: Christophe Choisy, Hubert Cecotti, Abdel Belaïd
• Keywords: Neural Network, PRIS 2004, PRIS 2007, Rotation Absorption, Rotation Invariant Features