Debarshi Dutta, Aruni Roy Chowdhury, U. Bhattacharya, S. K. Parui
{"title":"Building a Personal Handwriting Recognizer on an Android Device","authors":"Debarshi Dutta, Aruni Roy Chowdhury, U. Bhattacharya, S. K. Parui","doi":"10.1109/ICFHR.2012.189","DOIUrl":null,"url":null,"abstract":"The wide usage of touch-screen based mobile devices has led to a large volume of the users preferring touch-based interaction with the machine, as opposed to traditional input via keyboards/mice. To exploit this, we focus on the Android platform to design a personalized handwriting recognition system that is acceptably fast, light-weight, possessing a user-friendly interface with minimally-intrusive correction and auto-personalization mechanisms. Since cursive writing on smaller screens is not usual, here we study non-cursive handwriting only. The recognition is done at character level using nearest-neighbor matching to a small, automatically user-adaptive and dynamically updating library of character-class template gestures.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"504 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Frontiers in Handwriting Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFHR.2012.189","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The wide usage of touch-screen based mobile devices has led to a large volume of the users preferring touch-based interaction with the machine, as opposed to traditional input via keyboards/mice. To exploit this, we focus on the Android platform to design a personalized handwriting recognition system that is acceptably fast, light-weight, possessing a user-friendly interface with minimally-intrusive correction and auto-personalization mechanisms. Since cursive writing on smaller screens is not usual, here we study non-cursive handwriting only. The recognition is done at character level using nearest-neighbor matching to a small, automatically user-adaptive and dynamically updating library of character-class template gestures.