Research Article
Intelligent License Plate Recognition
@INPROCEEDINGS{10.1007/978-3-319-95153-9_25, author={Yaecob Gezahegn and Misgina Hagos and Dereje Gebreal and Zeferu Teklay Gebreslassie and G. Tekle and Yakob Haimanot}, title={Intelligent License Plate Recognition}, proceedings={Information and Communication Technology for Development for Africa. First International Conference, ICT4DA 2017, Bahir Dar, Ethiopia, September 25--27, 2017, Proceedings}, proceedings_a={ICT4DA}, year={2018}, month={7}, keywords={Filtering Edge detection Segmentation Recognition Artificial neural networks Vertical projection Template matching}, doi={10.1007/978-3-319-95153-9_25} }- Yaecob Gezahegn
Misgina Hagos
Dereje Gebreal
Zeferu Teklay Gebreslassie
G. Tekle
Yakob Haimanot
Year: 2018
Intelligent License Plate Recognition
ICT4DA
Springer
DOI: 10.1007/978-3-319-95153-9_25
Abstract
Road traffic accident is the leading cause of deaths and injuries in the world according to the orld ealth rganization (WHO). Every year many deaths and injuries are reported and most of them are in developing countries; the problem has great impact in Africa. ntelligent icense late ecognition and eporting (ILPR) plays an important role in minimizing traffic accidents by implementing traffic monitoring and management systems. Since the number of vehicles are increasing, breaking traffic rules, entering restricted areas are becoming a trend. So, to control these actions, a system which can recognize vehicles by their icense late (LP) is crucial. In this paper, we have developed ILPR system, which aims at reducing traffic accidents by processing an input image of a vehicle and reporting on its legality status. The ILPR starts with preprocessing and then extracts the LP using edge detection and vertical projection algorithms. To identify the icense late umber (LPN), characters found on the LP are extracted and recognized by rtificial eural etworks (ANN), which we trained with sample characters. If the recognized LPN is found to be a suspect after cross checking it with a pre-stored database, it will be sent to a person in charge via hort essage ervice (SMS). In the recognition part, different papers use template matching, but is sensitive to noise. In order to mitigate the noise problem, our system uses ANN. We have also added SMS module. The system is implemented using MATLAB and Java.
