Survey and bibliography of Arabic optical text recognition
Signal Processing
Preliminary investigation of techniques for automated reading of unformatted text
Communications of the ACM
Robust license plate segmentation method based on texture features and radon transform
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A practical license plate recognition system for real-time environments
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Automatic license plate recognition system based on color image processing
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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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 paper, 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 referred to as Vision System Development Platform (VSDP). Fixed filter, Minimum filter, Median Filter and Homomorphic Filtering are used in image enhancement process. After applying image enhancement, the image is segmented using blob analysis, 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 total of blobs, location, height and width, are being analyzed 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 using 3×3 kernel masks and 128 grayscale offset plus a new way (3D method) to calculate run length smoothing algorithm (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 experiments, analysis of error tables based on segmentation errors were constructed. The prototyped system has an accuracy more than 96% and suggestions to further improve the system are discussed in this paper pertaining to analysis of the error.