Abstract
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.
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Li, J., Niu, C., Fan, M. (2012). Multi-scale Convolutional Neural Networks for Natural Scene License Plate Detection. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_13
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DOI: https://doi.org/10.1007/978-3-642-31362-2_13
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