IIIT Hyderabad Publications |
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Online Handwriting Recognition using Depth SensorsAuthors: Rajat Aggarwal,Swetha Sirnam,Anoop Namboodiri,Jayanthi Sivaswamy, C V Jawahar Conference: International Conference on Document Analysis and Recognition (ICDAR 2015 2015) Date: 2015-08-23 Report no: IIIT/TR/2015/35 AbstractIn this work, we propose an online handwriting solution, where the data is captured with the help of depth sensors. Users may write in the air and our method recognizes it in real time using the proposed feature representation. Our method uses an efficient fingertip tracking approach and reduces the necessity of pen-up/pen-down switching. We validate our method on two depth sensors, Kinect and Leap Motion Controller. On a dataset collected from 20 users, we achieve a recognition accuracy of 97.59% for character recognition. We also demonstrate how this system can be extended for lexicon recognition with reliable performance. We have also prepared a dataset containing 1,560 characters and 400 words with the intention of providing common benchmark for handwritten character recognition using depth sensors and related research. Full paper: pdf Centre for Visual Information Technology |
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