边缘智能背景下的手写数字识别

随着边缘智能的快速发展，现有手写数字识别卷积网络模型的发展已越来越不适应边缘部署、算力下降的要求，且存在小样本泛化能力较差和网络训练成本较高等问题。借鉴卷积神经网络（CNN）经典结构、Leaky_ReLU算法、dropout算法和遗传算法及自适应和混合池化思想构建了基于LeNet-DL改进网络的手写数字识别模型，分别在大样本数据集MNIST和小样本真实数据集REAL上与LeNet、LeNet+sigmoid、AlexNet等算法进行对比实验。改进网络的大样本识别精度可达99.34%，性能提升约0.83%；小样本识别精度可达78.89%，性能提升约8.34%。实验结果表明，LeNet-DL网络相较于传统CNN在大样本和小样本数据集上的训练成本更低、性能更优且模型泛化能力更强。

Handwritten numeral recognition under edge intelligence background

With the rapid development of edge intelligence, the development of existing handwritten numeral recognition convolutional network models has become less and less suitable for the requirements of edge deployment and computing power declining, and there are problems such as poor generalization ability of small samples and high network training costs. Drawing on the classic structure of Convolutional Neural Network (CNN), Leaky_ReLU algorithm, dropout algorithm, genetic algorithm and adaptive and mixed pooling ideas, a handwritten numeral recognition model based on LeNet-DL improved convolutional neural network was constructed. The proposed model was compared on large sample MNIST dataset and small sample REAL dataset with LeNet, LeNet+sigmoid, AlexNet and other algorithms. The improved network has the large sample identification accuracy up to 99.34%, with the performance improvement of about 0.83%, and the small sample recognition accuracy up to 78.89%, with the performance improvement of about 8.34%. The experimental results show that compared with traditional CNN, LeNet-DL network has lower training cost, better performance and stronger model generalization ability on large sample and small sample datasets.