{"title":"用于人机交互的实时手写识别系统","authors":"Phuong Le, V. Nguyen","doi":"10.1109/ICCAIS.2013.6720530","DOIUrl":null,"url":null,"abstract":"This paper proposes a real time finger writing recognition system using a web camera to enable user to interact with computer systems by their own bare hands. The hand region is firstly extracted by background subtraction and blob labeling. Then, convex hull and convexity defect are used to count the fingers and detect the fingertip coordinates. The trajectory of the fingertip is smoothed and drawn to become a finger writing character. Next, it is recognized by the Support Vector Machine classifier, which is trained with our collected database and specific parameters for finger writing recognition. Finally, the event corresponding to the character is sent to the computer systems. Experimental results show that the proposed system can successfully count the fingers and recognize the finger writing English characters with the accuracies of 98.3% and 83.4% respectively.","PeriodicalId":347974,"journal":{"name":"2013 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Real time finger writing recognition system for human computer interaction\",\"authors\":\"Phuong Le, V. Nguyen\",\"doi\":\"10.1109/ICCAIS.2013.6720530\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a real time finger writing recognition system using a web camera to enable user to interact with computer systems by their own bare hands. The hand region is firstly extracted by background subtraction and blob labeling. Then, convex hull and convexity defect are used to count the fingers and detect the fingertip coordinates. The trajectory of the fingertip is smoothed and drawn to become a finger writing character. Next, it is recognized by the Support Vector Machine classifier, which is trained with our collected database and specific parameters for finger writing recognition. Finally, the event corresponding to the character is sent to the computer systems. Experimental results show that the proposed system can successfully count the fingers and recognize the finger writing English characters with the accuracies of 98.3% and 83.4% respectively.\",\"PeriodicalId\":347974,\"journal\":{\"name\":\"2013 International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAIS.2013.6720530\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Control, Automation and Information Sciences (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAIS.2013.6720530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real time finger writing recognition system for human computer interaction
This paper proposes a real time finger writing recognition system using a web camera to enable user to interact with computer systems by their own bare hands. The hand region is firstly extracted by background subtraction and blob labeling. Then, convex hull and convexity defect are used to count the fingers and detect the fingertip coordinates. The trajectory of the fingertip is smoothed and drawn to become a finger writing character. Next, it is recognized by the Support Vector Machine classifier, which is trained with our collected database and specific parameters for finger writing recognition. Finally, the event corresponding to the character is sent to the computer systems. Experimental results show that the proposed system can successfully count the fingers and recognize the finger writing English characters with the accuracies of 98.3% and 83.4% respectively.