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
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning-Based License Plate Detection Using Global and Local Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
The Application of a Convolution Neural Network on Face and License Plate Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
Human tracking using convolutional neural networks
IEEE Transactions on Neural Networks
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automatic license plate recognition
IEEE Transactions on Intelligent Transportation Systems
License Plate Recognition From Still Images and Video Sequences: A Survey
IEEE Transactions on Intelligent Transportation Systems
Hi-index | 0.00 |
We consider the problem of license plate detection in natural scenes using Convolutional Neural Network (CNN). CNNs are global trainable multi-stage architectures that automatically learn shift invariant features from the raw input images. Additionally, they can be easily replicated over the full input making them widely used for object detection. However, such detectors are currently limited to single-scale architecture in which the classifier only use the features extracted by last stage. In this paper, a multi-scale CNN architecture is proposed in which the features extracted by multiple stages are fed to the classifier. Furthermore, additional subsampling layers are added making the presented architecture also easily replicated over the full input. We apply the proposed architecture to detect license plates in natural sense images, and it achieves encouraging detection rate with neither handcrafted features nor controlling the image capturing process.