| Author: | Tam, Tai-cheung |
| Title: | A new stroke extraction method for handprinted Chinese character recognition |
| Degree: | M.Sc. |
| Year: | 1997 |
| Subject: | Chinese language -- Data processing Chinese character sets (Data processing) Input design, Computer Information storage and retrieval systems Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electronic Engineering |
| Pages: | viii, 88, [25] leaves : ill. ; 30 cm. + computer disk |
| Language: | English |
| Abstract: | In this project, a great deal of literature study and survey in the areas of handprinted Chinese character processing and recognition had been carried out. The knowledge and experience of the researchers in these areas are absorbed and hence a simple but effective handprinted Chinese character stroke extraction and recognition system is aimed to be devised and developed. The algorithms developed are attempted to be simple enough that they can be implemented on embedded microcontroller systems for postage address recognition and discrimination. The database of reference character library therefore need not be too large. So, the first 1,000 characters in the Eten 24 x 24 pixels font were chosen for testing. At first, an algorithm is developed to extract strokes without carrying out the thinning process because almost all thinning algorithm would cause spurious branches, splitting of the cross-point or distortion around junctions. It performs quite well for restricted handprinted characters, i.e. characters with clear separable strokes. But its performance is not good for unrestricted or distorted characters. Later on, strokes are thinned before extraction. To do that, a simple thinning method is employed and modification is made specially for Chinese characters. A stroke extraction algorithm is also developed for stroke segmentation and |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14201859.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.95 MB | Adobe PDF | View/Open |
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