Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.contributor | Department of Electronic Engineering | en_US |
| dc.creator | Lee, Kin-sang Timothy | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/1223 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Character recognition using digitally implementable neural network | en_US |
| dcterms.abstract | The aim of this project is to investigate the use of digitally implementable neural networks in character recognition. This work begins with a study on the effect of using different types of activation functions for the multi-layer feed-forward neural network. The back-propagation learning algorithm is used for training all the networks that have been tested in this project. Apart from the conventional approach for character recognition that is used by neural network, an alternative approach based on function approximation have been studied and tested, and the performance is found to be satisfactory. The STBP and MS models suggested by Wang [5, 6] have been studied and tested. A 'modified' MS model is proposed to resolve the stability problem of the STBP and MS models. It is found that the 'modified' MS network can be trained to achieve the required accuracy very much faster than those conventional BP networks, and can solve the stability problem anticipated by the STBP and MS models. | en_US |
| dcterms.extent | iv, 86, 3 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1996 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Optical character recognition devices | en_US |
| dcterms.LCSH | Pattern perception | en_US |
| dcterms.LCSH | Neural networks (Computer science) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15554405.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.91 MB | Adobe PDF | View/Open |
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