License Plate Detection using Cluster Run Length Smoothing Algorithm (CRLSA)

S.N.H.S. Abdullah, M. Khalid, R. Yusof, and K. Omar (Malaysia)


License plate recognition, clustering, run length smoothing algorithm, thresholding. (a) (b) Figure 1. (a)Samples of common Malaysia license plates (b) Samples of special Malaysia license plates.


Vehicle license plate recognition has been intensively stud ied in many countries. Due to the different types of li cense plates being used, the requirement of an automatic license plate recognition system is different for each coun try. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, clustering, fea ture extraction and neural networks.The image processing library is developed in-house which referred to as Vision System Development Platform (VSDP). Fixed filter, Mini mum filter, Median Filter and Homomorphic Filtering are used in image enhancement process. After applying im age enhancement, the image is segmented using blob anal ysis, horizontal scan line profiles, clustering and run length smoothing algorithm approach to identify the location of the license plate. Thoroughly each image is transformed into blob objects and its important information such as to tal of blobs, location, height and width, are being ana lyzed for the purpose of cluster exercising and choosing the best cluster with winner blobs. Here, new algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) approach was applied to locate the license plate at the right position. CRLSA consisted of two separate new proposed algorithm which applied new edge detector algorithm us ing 3x3 kernel masks and 128 grayscale offset plus a new way (3D method) to calculate run length smoothing algo rithm (RLSA), which can improve clustering techniques in segmentation phase. Three separate experiments were performed; Cluster and Threshold value 130 (CT130) and CRLSA with Threshold value 1 (CCT1). From those ex periments, analysis of error tables based on segmentation errors were constructed. The prototyped system has an ac curacy more than 96% and suggestions to further improve the system are discussed in this paper pertaining to analysis of the error.

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