{"title":"基于人工神经网络的数字识别","authors":"Mrinal Paliwal, Punit Soni, Sharad Chauhan","doi":"10.1109/InCACCT57535.2023.10141703","DOIUrl":null,"url":null,"abstract":"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 %.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Digit Recognition using the Artificial Neural Network\",\"authors\":\"Mrinal Paliwal, Punit Soni, Sharad Chauhan\",\"doi\":\"10.1109/InCACCT57535.2023.10141703\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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 %.\",\"PeriodicalId\":405272,\"journal\":{\"name\":\"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/InCACCT57535.2023.10141703\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/InCACCT57535.2023.10141703","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 %.