Soft Comput DOI 10.1007/s00500-014-1245-5 METHODOLOGIES AND APPLICATION Shadow aware license plate recognition system Shaimaa Ahmed El-said © Springer-Verlag Berlin Heidelberg 2014 Abstract During recent years, license plate recognition have been widely used as a core technology for security or traffic applications such as in traffic surveillance, parking lot access control, and information management. In this paper, Shadow Aware License Plate Recognition (SALPR) system is proposed to recognize Egyptian LP. This system achieves high recognition rate through applying shadow detection and removal, rotation adjustment and using Multilayer percep- tron as a powerful tool to perform the recognition process. To show the efficiency of the proposed system, experiments have been done on numerous captured images including vari- ous types of vehicles with different lighting and noise effects. The experimental results yield 95.5 % recognition accuracy, the recognition process takes 1.6 s to recognize plate infor- mation. Most of the elapsed time used is for the license plate extraction and rotation adjustment. The results show the fea- sibility of the methodology followed in this paper. Perfor- mance comparison between SALPR and other LP recogni- tion techniques shows that for most of the cases, SALPR performs better than other techniques under different lighting conditions and it shows the high robustness of the proposed algorithm. Keywords License plate recognition · Shadow removal · Plate detection · Character segmentation · Skew correction · MLPNN · Egyptian license plates Communicated by V. Piuri. S. A. El-said (B ) Electronics and Communications Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt e-mail: eng.sahmed@windowslive.com 1 Introduction With new developments in computer and communications technologies the need for improving Intelligent Surveillance Systems technologies is becoming more significant. The main role of visual traffic surveillance is capturing traffic data, detecting accidents and safety management in general. Learning based license plate (LP) detection methods using different classifiers become very popular. The basic idea is to use a classifier to group the features extracted from the vehi- cle images into positive class (LP region) or negative class (non-LP region). A number of computational intelligence architectures, such as artificial neural networks, genetic pro- gramming, and genetic algorithms, were implemented for LPs detection. Recently, adaptive boosting (AdaBoost) and support vector machine have been widely used for LP detec- tion as they do not need a large number of parameters to obtain a decent classification performance. Moving vehicles are often extracted with their associated cast shadows after the application of background subtraction to traffic image sequences. This phenomenon may lead to object losses, object shape distortion of detected vehicle. In some other situations particularly when there are bunch of vehicles near each other shadow of one vehicle may partially or completely be on another vehicle and this results in misdetection of these two or a group of separate vehicles as one big vehicle. Prob- lems associated with occlusion would be created afterwards. As a result the performance of the surveillance system would be affected if the cast shadows are not detected and removed. 1.1 Shadow Shadow detection is an important aspect of most object detection and tracking algorithms. Shadows and shadings in images occur when objects occlude light from a light 123