基于脉冲神经网络的手写体数字识别

Handwritten Digit Recognition Based on Spiking Neural Network

针对传统手写体数字识别精度不高的问题,本文设计了一种基于脉冲神经神经网络的手写体数字识别算法。该算法首先对输入的手写体数字进行预处理,然后提取手写体数字的二值图像轮廓和笔画次序等结构特征,最后根据脉冲神经网络模型分类,预测测试集合样本。该算法在MNIST字体库里的数据进行实验,精度可达98.7%,优于其它识别算法。

Aiming at the problem of traditional handwritten digit recognition accuracy is not high,a handwritten digit recognition based on spiking neural network algorithm is proposed in this paper.The handwritten digit which is inputted should be preprocessed firstly and then be extracted the binary image contour and stroke order. Finally,according to the classification of spiking neural network model,the sample size of test set is predicted.The algorithm is tested on the data of MNIST font library,and the accuracy is up to 98.7%,which is better than other recognition algorithms.