基于深度神经网络的手写数字识别模拟研究

当前图像识别大多采用基于特征提取的传统机器学习方法与卷积神经网络的方法，但传统图像识别技术需要手动提取图片特征，而卷积神经网络对硬件要求高，训练时间长等。针对以上问题，本文提出基于深度神经网络模型的手写体图像识别方法,让机器自动学习特征，并在此基础上，通过改进成本函数，加入dropout防止过拟合，来提高手写数字识别的识别率。仿真实验对比结果表明，基于深度神经网络模型的方法比当前传统算法的识别率提高了3.41%，有效解决了人工识别费力耗时问题，对手写数字的研究具有重要意义。

Research on Handwritten Numeral Recognition Based on Deep Learning

The traditional machine learning method based on feature extraction and the convolutional neural network method are mostly used in image recognition at present. However, the traditional image recognition technology requires manual extraction of image features, while convolutional neural network has high requirements on hardware and long training time.In view of the above problems, this paper proposes a handwritten image recognition method based on the deep neural network model, which enables the machine to learn the features automatically. On this basis, it improves the recognition rate of handwritten numeral recognition by improving the cost function and adding dropout to prevent overfitting.The results of simulation experiment show that the method based on the deep neural network model has improved the recognition rate by 3.41% compared with the current traditional algorithm, which effectively solves the problem of labor and time consuming in manual recognition.