{"title":"手写多数字识别与机器学习","authors":"Soha Boroojerdi, George Rudolph","doi":"10.1109/ietc54973.2022.9796722","DOIUrl":null,"url":null,"abstract":"Offline handwritten digit recognition is a well-known problem that remains at best partially solved. This paper presents a study of three different algorithms for offline handwritten multi-digit recognition using the MNIST dataset: Decision Trees, Multilayer Perceptrons and Random Forest. Our results indicate that Random Forest had the best accuracy at 96% with reasonable runtime performance. This kind of study is not novel-however, the authors developed a mechanism for reading multi-digit numbers from image files and webcams that may be of interest.","PeriodicalId":251518,"journal":{"name":"2022 Intermountain Engineering, Technology and Computing (IETC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Handwritten Multi-Digit Recognition With Machine Learning\",\"authors\":\"Soha Boroojerdi, George Rudolph\",\"doi\":\"10.1109/ietc54973.2022.9796722\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Offline handwritten digit recognition is a well-known problem that remains at best partially solved. This paper presents a study of three different algorithms for offline handwritten multi-digit recognition using the MNIST dataset: Decision Trees, Multilayer Perceptrons and Random Forest. Our results indicate that Random Forest had the best accuracy at 96% with reasonable runtime performance. This kind of study is not novel-however, the authors developed a mechanism for reading multi-digit numbers from image files and webcams that may be of interest.\",\"PeriodicalId\":251518,\"journal\":{\"name\":\"2022 Intermountain Engineering, Technology and Computing (IETC)\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Intermountain Engineering, Technology and Computing (IETC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ietc54973.2022.9796722\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Intermountain Engineering, Technology and Computing (IETC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ietc54973.2022.9796722","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Handwritten Multi-Digit Recognition With Machine Learning
Offline handwritten digit recognition is a well-known problem that remains at best partially solved. This paper presents a study of three different algorithms for offline handwritten multi-digit recognition using the MNIST dataset: Decision Trees, Multilayer Perceptrons and Random Forest. Our results indicate that Random Forest had the best accuracy at 96% with reasonable runtime performance. This kind of study is not novel-however, the authors developed a mechanism for reading multi-digit numbers from image files and webcams that may be of interest.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
