An Algorithm for License Plate Recognition Applied to Intelligent Transportation System

Authors:
Ying Wen; Yue Lu; Jingqi Yan; Zhenyu Zhou;K. M. von Deneen; Pengfei Shi
Affiliations:
Columbia Univ., New York, NY, USA;-;-;-;-;-
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2011

Citing 0
Cited 6

Face recognition using difference vector plus KPCA

Digital Signal Processing
Short communication: An improved discriminative common vectors and support vector machine based face recognition approach

Expert Systems with Applications: An International Journal
Building a license plate recognition system for Vietnam tollbooth

Proceedings of the Third Symposium on Information and Communication Technology
Vehicle license plate localization and license number recognition using unit-linking pulse coupled neural network

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Performance improvement of character recognition in industrial applications using prior knowledge for more reliable segmentation

Expert Systems with Applications: An International Journal
Genetic algorithm based neural network for license plate recognition

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16% and 98.34%, respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5%, 98.6%, and 97.8%, respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54% when the system is used for LPR in various complex conditions.