caa a22 4500 605468435 CHVBK 20210128100316.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00500-014-1245-5 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1245-5 El-said Shaimaa Electronics and Communications Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt aut Shadow aware license plate recognition system [Elektronische Daten] [Shaimaa El-said] 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 perceptron 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 various types of vehicles with different lighting and noise effects. The experimental results yield 95.5% recognition accuracy, the recognition process takes 1.6s to recognize plate information. Most of the elapsed time used is for the license plate extraction and rotation adjustment. The results show the feasibility of the methodology followed in this paper. Performance comparison between SALPR and other LP recognition 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. Springer-Verlag Berlin Heidelberg, 2014 License plate recognition nationallicence Shadow removal nationallicence Plate detection nationallicence Character segmentation nationallicence Skew correction nationallicence MLPNN nationallicence Egyptian license plates nationallicence Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 225-235 1432-7643 19:1<225 2015 19 500 https://doi.org/10.1007/s00500-014-1245-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1245-5 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- El-said Shaimaa Electronics and Communications Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 225-235 1432-7643 19:1<225 2015 19 500