Abstract
In this paper, a novel self-creating disk-cell-splitting (SCDCS) algorithm is proposed for training the radial wavelet neural network (RWNN) model. Combining with the least square (LS) method which determines the linear weight coefficients, SCDCS can create neurons adaptively on a disk according to the distribution of input data and learning goals. As a result, a disk map is made for input data as well as a RWNN model with proper architecture and parameters can be decided for the recognition task. The proposed SCDCS-LS based RWNN model is employed for the recognition of license plate characters. Compared to the classical radial-basis-function (RBF) network with K-means clustering and LS, the proposed model can make a better recognition performance even with fewer neurons.
Highlights
Nowadays automatic license plate recognition (ALPR) plays an important role in many automated transport systems such as road traffic monitoring, automatic payment of tolls on highways or bridges and parking lots access control
The same is true of radial wavelet neural network, which is based on the Euclidean distance
When the number of neurons in the splitting process increases to N = 27 with the corresponding valid neurons number N1 = 26, the total success recognition rate for training set and testing set of self-creating disk-cell-splitting (SCDCS)-least square (LS) based radial wavelet neural network (RWNN) reaches 99.89% and 99.76% respectively, and after that the false recognition rate will no longer be significantly decrease with newly added neurons
Summary
Nowadays automatic license plate recognition (ALPR) plays an important role in many automated transport systems such as road traffic monitoring, automatic payment of tolls on highways or bridges and parking lots access control. The radial wavelet neural network (RWNN) is used as the means for plate character data classification. It is well-known that selecting an appropriate number of hidden neurons is crucial for good performance and the first task determining the architecture of the feedforward neural networks. The same is true of radial wavelet neural network, which is based on the Euclidean distance In this view, a novel self-creating and self-organizing algorithm is proposed to determine the number of hidden neurons. While for the translation parameters of radial wavelets as well as the number of neurons in the hidden layer, a novel self-organizing type SCDCS algorithm is employed.
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