Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Real Time Vehicle's License Plate Recognition System
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
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
Multiple License Plate Detection for Complex Background
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Learning-Based License Plate Detection Using Global and Local Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
Detecting, tracking and recognizing license plates
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A decision step for shape context matching
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
License Plate Recognition From Still Images and Video Sequences: A Survey
IEEE Transactions on Intelligent Transportation Systems
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
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This paper addresses a license plate detection and recognition (LPR) task on still images of trucks. The main contribution of our LPR system is the fusion of different segmentation algorithms used to improve the license plate detection. We also compare the performance of two kinds of classifiers for optical character recognition (OCR): one based on the a contrario framework using the shape contexts as features and the other based on a SVM classifier using the intensity pixel values as features.