Digital Image Processing
Color Texture-Based Object Detection: An Application to License Plate Localization
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
An efficient implementation of the Hough transform for detecting vehicle license plates using DSP'S
RTAS '95 Proceedings of the Real-Time Technology and Applications Symposium
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A hybrid License Plate Extraction Method Based On Edge Statistics and Morphology
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Multinational License Plate Recognition System: Segmentation and Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Multiple License Plate Detection for Complex Background
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
An efficient method of license plate location
Pattern Recognition Letters
Color image segmentation using density-based clustering
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Learning-Based License Plate Detection Using Global and Local Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Intelligent Software Measurement System for Automating the Goal-Question-Metrics Process
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Fast and robust text detection in images and video frames
Image and Vision Computing
License plate character segmentation using hidden markov chains
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Adaptive local binarization method for recognition of vehicle license plates
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Automatic license plate recognition system based on color image processing
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Automatic license plate recognition
IEEE Transactions on Intelligent Transportation Systems
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications
IEEE Transactions on Intelligent Transportation Systems
A comprehensive method for multilingual video text detection, localization, and extraction
IEEE Transactions on Circuits and Systems for Video Technology
Vehicle license plate detection algorithm based on color space and geometrical properties
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
An automated vision system for container-code recognition
Expert Systems with Applications: An International Journal
A multi-style license plate recognition system based on tree of shapes for character segmentation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A novel smart multi-license plate recognition method
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
UIT-ANPR: toward an open framework for automatic number plate recognition on smartphones
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Despite the success of license plate recognition (LPR) methods in the past decades, few of them can process multi-style license plates (LPs), especially LPs from different nations, effectively. In this paper, we propose a new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format. In the recognition procedure these four parameters are managed by relevant algorithms, i.e., plate rotation, plate line segmentation, character recognition and format matching algorithm, respectively. To recognize special style LPs, users can configure the method by defining corresponding parameter values, which will be processed by the relevant algorithms. In addition, the probabilities of the occurrence of every LP style are calculated based on the previous LPR results, which will result in a faster and more precise recognition. Various LP images were used to test the proposed method and the results proved its effectiveness.