Abstract
License plate detection is a crucial part in license plate recognition systems and is often considered as a solved problem. However, there are still plenty of complex scenes where the current methods are invalidated. In order to increase the performance in these scenes, we propose a novel character-based method to detect multiple license plates in complex images. Firstly, a preprocessing step is performed. Then we use a modified maximally stable extremal region (MSER) based detector called MSER-+ to detect the possible character regions. Some of the regions are removed according to their geographical information. Hierarchical morphology helps to connect candidate MSERs of various sizes. The regions satisfying some geographical limits will be fed into a convolutional neural network (CNN) model for further verification. Extensive experimental results validate that our method works well in a large variety of complex scenes.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (no. 61472393). The authors would like to thank Zeruo Liu for providing us with plenty of photos taken in complex scenes.
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Li, D., Wang, Z. (2016). A Character-Based Method for License Plate Detection in Complex Scenes. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_47
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DOI: https://doi.org/10.1007/978-981-10-3005-5_47
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