Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.creator | Tsang, Chi-hang Peter | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/241 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | On-line recognition of digits and alphabets | en_US |
| dcterms.abstract | Handwriting is a natural and fast way to input words to computers. Computer recognition of on-line handwriting offers a new way of improving the human-computer interface. The objective of this project is to study and implement efficient algorithms for recognising handwriting digits and alphabets. The system includes handwriting character capturing, feature extraction, off-line/on-line neural network training, and recognition of handwriting characters. Features used for recognition can be categorised into two groups: off-line and on-line features. On-line recognition makes use of pen trajectory data. Also spatially overlapping characters do not pose a serious segmentation problem as they are still separable by using the on-line information. On the other hand, on-line recognition should have a reliable and robust recognition unit. This can be achieved by using both on-line and off-line features, which are discussed in this dissertation. A multi-layer feedforward neural network is trained to recognise 62 handwriting characters (10 digits, 26 lowercase alphabets, and 26 uppercase alphabets) using the error back-propagation algorithm with a training data set of five samples for each character. Different weight vectors are trained for different writers. Experimental results show that the system can achieve a correct rate of about 89% for user-dependent recognition. | en_US |
| dcterms.alternative | Online recognition of digits and alphabets | - |
| dcterms.extent | vi, 81 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2000 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Optical pattern recognition | en_US |
| dcterms.LCSH | Writing -- Data processing | en_US |
| dcterms.LCSH | Imaging processing | 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 | |
|---|---|---|---|---|
| b15276727.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.45 MB | Adobe PDF | View/Open |
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