基于人工神经网络的数字识别

Mrinal Paliwal, Punit Soni, Sharad Chauhan
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摘要

本文讨论了基于人工神经网络的数字识别方法。由于有大量的数据和算法,神经网络现在可以用来训练网络并得到想要的结果。随着信息和通信技术的进步,互联网的使用也随着技术的使用而增加,对数字识别系统的需求也越来越普及。本文将讨论一种数字识别技术。我们将使用MNIST数据集训练我们的模型,然后测试我们的模型。Python编程用于执行数字识别。我们有一个包含28000个数字图像的数据集,将用于训练,14000个数字图像用于测试。多层人工神经网络的测试性能准确率为99.59%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digit Recognition using the Artificial Neural Network
Digit recognition using the Artificial Neural Network method is discussed in this study. Due to the enormous volumes of data and algorithms, the neural network can now be used to train the network and get the desired result. With the advancement in information and communication technology, internet access has increased as the use of technology increases the demand for digit recognition systems has gained popularity. This paper will discuss one of the techniques for digit recognition. We will train our model with the MNIST dataset & then test our model. Programming in Python is used to perform digit recognition. We have taken a dataset of 28,000-digit images, that will be used for training and 14,000-digit images for testing. The test performance accuracy of our multi-layer artificial neural network is 99.59 %.
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