License plate recognition (LPR) is composed of license plate detection (LPD), character segmentation, and character recognition. In this paper, we put forward a license plate recognition (LPR) system. For license plate detection (LPD), a hybrid algorithm based on colour and shape is proposed (HLPD). For character segmentation, taking into account the horizontal preliminary information of the black and white histogram, a cross-zero search character segmentation algorithm based on the black and white histogram is suggested (CCZA). For character recognition, 10 sets of CNN models with different frameworks are proposed. The CCCP-BN-DP performs best when compared to the performance of the test set. The HLPD-CCZA-CCCP-BN-DP system is then applied to the public data set CCPD and experiments demonstrate the effectiveness of the LPR system.


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    Title :

    License plate recognition system based on the difference of convolutional neural network framework


    Contributors:
    Li, Zhihong (author) / Zhang, Jing (author) / Wen, Yanjie (author)


    Publication date :

    2022-10-08


    Size :

    722228 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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