Abstract
Although the license plate recognition system has been widely used, the location and recognition rate is still affected by the clarity and illumination conditions. A license plate locating (LPL) method and a license plate characters recognition (LPCR) method, respectively, based on convolution neural network (CNN) and Length_Feature (LF), are proposed in this paper. Firstly, this paper changes the activation function of CNN, and extracts local feature to train the network. Through this change, the network convergence has sped up, the location accuracy has improved, and wrong location and long time consuming, which caused by some complicated factors such as light conditions, fuzzy image, tilt, complex background and so on, have been resolved. Secondly, the LF, which is proposed in this paper, is easier to understand and has less calculation and higher speed than transform domain features, and also has higher accuracy to recognize fuzzy and sloping characters than traditional geometric features.
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He, S., Yang, C., Pan, JS. (2016). The Research of Chinese License Plates Recognition Based on CNN and Length_Feature. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_33
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