{"title":"一种拓扑结构构建方法及其在离线手写数字识别中的应用","authors":"Huichuan Duan, Jinwei Yang, Xiyu Liu, Hong Liu","doi":"10.1109/ICPCA.2008.4783609","DOIUrl":null,"url":null,"abstract":"In searching for an adequate feature extraction approach for pen-like smart scanners to scan and recognize handwritten digits, the authors propose a topological structure construction approach to help extracting features served for recognition. Unlike most of the feature extraction approaches that simply focus on the pixels constituting the visual images of digits, the proposed approach tries in a different direction, that is, adding some lines of pixels to the images and counting the topological structures in the newly formed images. Experimental results show that for samples collected from students' assignments, 9 features is enough for a 93.5% recognition rate, with the help of a suitable classification tree.","PeriodicalId":244239,"journal":{"name":"2008 Third International Conference on Pervasive Computing and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Topological Structure Construction Approach and its Application in Off-line Handwritten Digit Recognition\",\"authors\":\"Huichuan Duan, Jinwei Yang, Xiyu Liu, Hong Liu\",\"doi\":\"10.1109/ICPCA.2008.4783609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In searching for an adequate feature extraction approach for pen-like smart scanners to scan and recognize handwritten digits, the authors propose a topological structure construction approach to help extracting features served for recognition. Unlike most of the feature extraction approaches that simply focus on the pixels constituting the visual images of digits, the proposed approach tries in a different direction, that is, adding some lines of pixels to the images and counting the topological structures in the newly formed images. Experimental results show that for samples collected from students' assignments, 9 features is enough for a 93.5% recognition rate, with the help of a suitable classification tree.\",\"PeriodicalId\":244239,\"journal\":{\"name\":\"2008 Third International Conference on Pervasive Computing and Applications\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Third International Conference on Pervasive Computing and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPCA.2008.4783609\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Third International Conference on Pervasive Computing and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPCA.2008.4783609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Topological Structure Construction Approach and its Application in Off-line Handwritten Digit Recognition
In searching for an adequate feature extraction approach for pen-like smart scanners to scan and recognize handwritten digits, the authors propose a topological structure construction approach to help extracting features served for recognition. Unlike most of the feature extraction approaches that simply focus on the pixels constituting the visual images of digits, the proposed approach tries in a different direction, that is, adding some lines of pixels to the images and counting the topological structures in the newly formed images. Experimental results show that for samples collected from students' assignments, 9 features is enough for a 93.5% recognition rate, with the help of a suitable classification tree.