A New Text Detection Approach Based on BP Neural Network for Vehicle License Plate Detection in Complex Background

Authors:
Yanwen Li;Meng Li;Yinghua Lu;Ming Yang;Chunguang Zhou
Affiliations:
Computer School, Northeast Normal University, Changchun, Jilin Province, China and College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China and Key Laboratory for Applied Statistics of MOE, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China;Computer School, Northeast Normal University, Changchun, Jilin Province, China and Key Laboratory for Applied Statistics of MOE, China;College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 4
Cited 1

Multi-resolutional gabor filter in texture analysis

Pattern Recognition Letters
Digital Image Processing

Digital Image Processing
Multiple License Plate Detection for Complex Background

AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Fast and robust text detection in images and video frames

Image and Vision Computing

License Plate Detection Using Neural Networks

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the development of Intelligent Transport Systems (ITS), automatic license plate recognition (LPR) plays an important role in numerous applications in reality. In this paper, a coarse to fine algorithm to detect license plates in images and video frames with complex background is proposed. First, the method based on Component Connect (CC) is used to detect the possible license plate regions in the coarse detection. Second, the method based on texture analysis is applied in the fine detection. Finally, a BP Neural Network is adopted as classifier, parts of the features is selected based on statistic diagram to make the network efficient. The average accuracy of detection is 95.3% from the images with different angles and different lighting conditions.