| Author: | Tsang, Chi-hang Peter |
| Title: | On-line recognition of digits and alphabets |
| Other Title: | Online recognition of digits and alphabets |
| Degree: | M.Sc. |
| Year: | 2000 |
| Subject: | Optical pattern recognition Writing -- Data processing Imaging processing Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Electronic and Information Engineering |
| Pages: | vi, 81 leaves : ill. ; 30 cm |
| Language: | English |
| 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. |
| Rights: | All rights reserved |
| Access: | restricted access |
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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