In pattern recognition applications, it is usually important that the same identification be given for a pattern, independent of a variety of positions, rotations and /or distortions of the pattern within the recognition device's field of view. This research relates to development of a preprocessor for a neural network character recognition system, where the role of the preprocessor is to assist in minimizing the difficulties related to variations of position and rotations of a character within the field of view. The preprocessor explored here was suggested in 1970' (Lendaris & Stanly, 1970), and is implemented here with more recent advances in neural network and discrete computation technologies.
The preprocessor consists of calculating the two-dimensional Fourier transform of the image (current hardware technology allows this to occur in less than 100 ms for a 256x256 pixels image , on a PC based machine with accelerator card), and then taking certain measurements on the transformed image. These measurements are given to the neural network, which processes the data to provide the character identification. Introduction of the preprocessor is shown to yield a great reduction in sensitivity to image translation and/or rotation.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-5210 |
| Date | 01 January 1991 |
| Creators | Du, Daqiao |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
Page generated in 0.0101 seconds