Character Rotation Absorption Using a Dynamic Neural Network Topology: Comparison With Invariant Features
Year: 2004
Type: article
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 £¥ ¤ problem to a ¦§¤ problem, that is easier to treat. These proposals are applied on a synthetic and on a real EDF 1 base of multi-oriented characters. A comparison is made with Fourier and Fourier-Mellin invariants. 1
Institution: READ Global
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1632036656