【摘要】 铝轮毂背腔字符分辨率较低、背景噪声较大,对其进行识别时不易提取几何特征和纹理特征。为此,提出一种基于改进卷积神经网络(CNN)的字符识别方法。在原始CNN的基础上引入改进的inception结构对网络构架进行优化,以提升计算资源的利用率,并在保持网络计算资源不变的前提下增加网络的宽度和深度,降低字符识别时间。实验结果表明,该方法训练准确率达99%以上,识别准确率达98.5%,识别效果优于支持向量机、BP神经网络等方法。更多还原

【Abstract】 It is difficult to extract geometric and texture features when recognizing the characters in the aluminium wheel back cavity because of its low resolution and strong background noise.Therefore,a character recognition method based on improved Convolution Neural Network(CNN) is proposed.On the basis of the original CNN,an improved inception structure is introduced to optimize the network architecture to improve the utilization of computing resources,increase the width and depth of the network and reduce the time of character recognition while keeping the network computing resources unchanged.Experimental results show that the training accuracy of this method is over 99% and the recognition accuracy is 98.5%,which is better than that of Support Vector Machine(SVM) and BP neural network.更多还原