{"title":"无眼触摸屏键盘打字的可行性","authors":"K. Vertanen, Haythem Memmi, P. Kristensson","doi":"10.1145/2513383.2513399","DOIUrl":null,"url":null,"abstract":"Typing on a touchscreen keyboard is very difficult without being able to see the keyboard. We propose a new approach in which users imagine a Qwerty keyboard somewhere on the device and tap out an entire sentence without any visual reference to the keyboard and without intermediate feedback about the letters or words typed. To demonstrate the feasibility of our approach, we developed an algorithm that decodes blind touchscreen typing with a character error rate of 18.5%. Our decoder currently uses three components: a model of the keyboard topology and tap variability, a point transformation algorithm, and a long-span statistical language model. Our initial results demonstrate that our proposed method provides fast entry rates and promising error rates. On one-third of the sentences, novices' highly noisy input was successfully decoded with no errors.","PeriodicalId":378932,"journal":{"name":"Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"The feasibility of eyes-free touchscreen keyboard typing\",\"authors\":\"K. Vertanen, Haythem Memmi, P. Kristensson\",\"doi\":\"10.1145/2513383.2513399\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Typing on a touchscreen keyboard is very difficult without being able to see the keyboard. We propose a new approach in which users imagine a Qwerty keyboard somewhere on the device and tap out an entire sentence without any visual reference to the keyboard and without intermediate feedback about the letters or words typed. To demonstrate the feasibility of our approach, we developed an algorithm that decodes blind touchscreen typing with a character error rate of 18.5%. Our decoder currently uses three components: a model of the keyboard topology and tap variability, a point transformation algorithm, and a long-span statistical language model. Our initial results demonstrate that our proposed method provides fast entry rates and promising error rates. On one-third of the sentences, novices' highly noisy input was successfully decoded with no errors.\",\"PeriodicalId\":378932,\"journal\":{\"name\":\"Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2513383.2513399\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2513383.2513399","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The feasibility of eyes-free touchscreen keyboard typing
Typing on a touchscreen keyboard is very difficult without being able to see the keyboard. We propose a new approach in which users imagine a Qwerty keyboard somewhere on the device and tap out an entire sentence without any visual reference to the keyboard and without intermediate feedback about the letters or words typed. To demonstrate the feasibility of our approach, we developed an algorithm that decodes blind touchscreen typing with a character error rate of 18.5%. Our decoder currently uses three components: a model of the keyboard topology and tap variability, a point transformation algorithm, and a long-span statistical language model. Our initial results demonstrate that our proposed method provides fast entry rates and promising error rates. On one-third of the sentences, novices' highly noisy input was successfully decoded with no errors.