卷积神经网络的优化在车牌号识别上的运用

通过与传统神经网络对比,分析了利用卷积神经网络(CNN)进行车牌号图像识别中的特征提取过程,提出了优化卷积和池化的过程来提高算法的收敛速度和准确率。运用Py Charm环境建立了改进后的车牌号识别模型,并通过实验验证了其正确性与识别速度。通过BP神经网络、传统Le Net-5 CNN和改进后的CNN对相同的字符集进行对比分析实验,得出了改进后的CNN模型的优势。

Optimization of Convolutional Neural Network for License Plate Recognition

Compared with the traditional neural network,this paper analyzes the feature extraction process in image recognition of autombile plate number using convolutional neural network,and proposes the process of optimizing convolution and pooling to improve the convergence speed and accuracy of the algorithm.The improved license plate recognition model is established in PyCharm environment,and its correctness and recognition speed are verified.By comparing the same character set with BP neural network,conventional LeNet-5 CNN and the modified convolution neural network,the advantages of the proposed model are obtained.