Pengesanan nombor plat kenderaan menggunakan alkhwarizmi gugusan dan kelancaran jarak larian(GKJL)
Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this article, an automatic license plate recognition system is...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2009
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| Online Access: | http://journalarticle.ukm.my/39/1/ http://journalarticle.ukm.my/39/ http://www.ukm.my/~jsm/contents.html |
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| Summary: | Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this article, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, clustering, feature extraction and neural networks. The image processing library is developed in-house which is referred to as Vision System Development Platform (VSDP). Sharpen filter, Minimum filter, Median Filter and Homomorphic Filter were used in the image enhancement process. After applying image enhancement, the image is segmented using blob analysis, horizontal scan line profiles, clustering and run length smoothing algorithms approach to identify the location of the license plate. Thoroughly each image is transformed into blob objects and its important information such as total bumber of blobs, location, height and width, are being analyzed for the purpose of cluster exercising and choosing the best cluster with winner blobs. A new algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) approach was applied to locate the license plate at the right position. CRLSA consists of two separate proposed algorithm which applied proposed edge detector algorithm using 3×3 kernel masks and 128 grayscale offset, and the resulting image is thresholded in order to calculate Run Length Smoothing Algorithm (RLSA), which has shown to improve the clustering process in the segmentation phase. Three separate experiments were performed to analyse its effectiveness. From those experiments, analysis of error tables were constructed. The prototyped system has an accuracy of more than 96% and suggestions to further improve the system are also discussed. |
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