Title :
Accuracy Enhancement for License Plate Recognition
Author :
Zheng, Lihong ; He, Xiangjian ; Samali, Bijan ; Yang, Laurence T.
Author_Institution :
Sch. of Comput. & Math., Charles Sturt Univ., Albury, NSW, Australia
fDate :
June 29 2010-July 1 2010
Abstract :
Automatic License Plate Recognition is useful for real time traffic management and surveillance. License plate recognition usually contains two steps, namely license plate detection/localization and character recognition. Recognizing characters in a license plate is a very difficult task due to poor illumination conditions and rapid motion of vehicles. When using an OCR for character recognition, it is crucial to correctly remove the license plate boundaries after the step for license plate detection. No matter which OCRs are used, the recognition accuracy will be significantly reduced if the boundaries are not properly removed. This paper presents an efficient algorithm for non character area removal. The algorithm is based on the license plates detected using an AdaBoost algorithm. Then it follows the steps of character height estimation, character width estimation, segmentation and block identification. The algorithm is efficient and can be applied in real-time applications. The experiments are performed using OCR software for character recognition. It is shown that much higher recognition accuracy is obtained by gradually removing the license plate boundaries.
Keywords :
character recognition; estimation theory; image segmentation; object detection; object recognition; traffic engineering computing; vehicles; AdaBoost algorithm; OCR; accuracy enhancement; block identification; character height estimation; character recognition; character segmentation; character width estimation; illumination condition; license plate detection; license plate localization; license plate recognition; real time traffic management; traffic surveillance; Accuracy; Character recognition; Classification algorithms; Histograms; Image segmentation; Licenses; Pixel; CCA; LPR; blob extraction; image segmentation;
Conference_Titel :
Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on
Conference_Location :
Bradford
Print_ISBN :
978-1-4244-7547-6
DOI :
10.1109/CIT.2010.111