In this paper a learning algorithm of synergetic neural network based on selective attention parameters is proposed. According to the mechanism of the Human Visual System (HVS), the weight matrix of synergetic neural network can be obtained by multiplying the prototype matrix by selective attention parameters. Two selective attention models based on the human visual system are put forward in this paper. The comparative experiments between the traditional algorithm SCAP and the new method we proposed in the application of recognizing the real gray images of numeric and alphabetic characters are done. And the results show that our method can improve the synergetic neural network's recognition performance and be more suitable to human visual system.
In this paper, a modified templet matching method is proposed. Based on traditional templet matching method, the modified templet is desighed to achieve a more effective result by extending and strengthening the feature region of the characters. This method is applied to recognize characters in car license plate database of Hu-ning highway and the recognition rate is 97.1%. Experimental results show that the modified templet matching method has higher recognition rate and robustness.
The binarization of license plate image is one of the key techniques of car license plate recognition (CLPR) system and its results influence the accuracy of the segmentation of characters and their identification directly. In this paper, by analyzing the limitations of Otsu's method and Bernsen's method, a practical method of license plate binarization based on histogram analysis is proposed. In this method, the feature that the percentage of the character area is always less than that of background is presented to distinguish the style of plate. Then a global thresholding method, Doyle's method, is used to threshold the plate image. By counting over 8,000 pieces of plate images, the accuracy is nearly 99%. Only those pictures which are badly polluted or with very low resolution cannot be binarized correctly. The experimental and field-tested results show that our method has higher accuracy, higher speed and better binarization effect. The method has been applied in our CLPR system successfully.
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