文章基本信息

标题：LIBYAN VEHICLE PLATE RECOGNITION USING REGION-BASED FEATURES AND PROBABILISTIC NEURAL NETWORK
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作者：KHADIJA AHMAD JABAR MOHAMMAD FAIDZUL NASRUDIN
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：94
期号：1
出版社：Journal of Theoretical and Applied
摘要：Automatic License Plate Recognition (ALPR) has wide range of commercial applications such as finding stolen cars, controlling access to car parks and gathering traffic flow statistics. Existing Libyan License Plate Recognition (LLPR) methods are not presented promising results due to their inefficient features for the extracted characters and numbers. In this work, an improved LLPR method is presented. The method is composed of five stages: pre-processing, license plate extraction, character and numbers segmentation, feature extraction and license plate recognition. In the pre-processing, undesired data, such as background noises are removed. Then, the license plate is extracted using few mathematical morphologies, Connected Component Analysis (CCA) and Region of Interest (ROI) extraction. After that, characters and numbers from the image regions of the license plate are extracted. A combination of geometrical features and Gabor features are considered to represent each of the character and word in the plates. Then, the recognition is done by using a template matching and a Probabilistic Neural Network (PNN) classification. The performance of the proposed method is evaluated and tested using 100 self-collected images of Libyan national license plates. The experimental results have shown that the proposed method has produced promising results and superior than other existing methods.
关键词：Automatic license plate recognition; Image processing; Feature extraction; Probabilistic Neural network.