The operation of a complete license plate recognition system includes three parts: license plate localization, character segmentation, and character identification. Among these three parts, license plate localization is relatively more difficult and complicated. Until now, differentiating background and real license plate images in real and random traffic conditions remains to be a very difficult task. Via a VQ coding technique, this study introduces a method resolve this problem. As a preprocessing step, this method first converts an image to be classified into binary form by using statistics generated from a license plate image database. The next step of the proposed approach is to use a VQ method to represent the image by a series of codewords. By computing the probability of these codewords used by the license plate and background images, these codewords are renumbered. By using neural networks to classify such images, our experimental results show that the proposed approach can differentiate background and real license plate images with a very high successful rate.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0716107-155550 |
| Date | 16 July 2007 |
| Creators | Lai, Jui-Min |
| Contributors | none, Chen-Wen Yen, none |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716107-155550 |
| Rights | unrestricted, Copyright information available at source archive |
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