基于CNN优化的手写数字识别技术研究

A Study on Handwritten Digital Recognition Technology Based on CNN Optimization

在深度学习领域中,手写数字识别是通过对大量的手写数字数据集的训练和测试、权重和偏置的优化,从而达到设计良好的网络模型。针对手写数字的形式多样性,从DNN和CNN网络结构入手,分析Le Net5的实际效果,采用Adam优化器和最佳的学习步长,实现了良好的识别效果。实验结果表明,论文所提方法能够显著提高手写数字的识别精度,对数字识别领域有重要的研究意义和商业价值。

In the field of deep learning,handwritten digital recognition is designed by training and testing many handwritten digital data sets,optimizing weights and biases to achieve a well-designed network model. According to the variety of handwritten digits, starting with the DNN and CNN network structure,the actual effect of the LeNet5 is analyzed,and the Adam optimizer and the best learning step are used to achieve a good recognition effect.The experimental results show that the proposed method can significantly improve the recognition accuracy of handwritten digits and has important research significance and commercial value in the field of digital recognition.