Magic Pen: automatic pen-mode switching for document annotation
| dc.contributor.advisor | Collins, Christopher | |
| dc.contributor.author | Desousa, Kevin A. | |
| dc.date.accessioned | 2022-08-29T18:28:20Z | |
| dc.date.available | 2022-08-29T18:28:20Z | |
| dc.date.issued | 2022-08-01 | |
| dc.degree.discipline | Computer Science | en |
| dc.degree.level | Master of Science (MSc) | en |
| dc.description.abstract | Traditional digital pen interfaces use menu buttons to change pen modes, resulting in time and cognitive load spent on round-trips and potential errors from tapping small mode selection buttons. This thesis presents Magic Pen, a technique that automatically switches between digital pen modes. The Magic Pen system is driven by a Long Short-Term Memory (LSTM) model trained on pen data collected from nine participants and uses Transfer Learning (TL) to tune itself towards a user’s specific annotations iteratively. If Magic Pen chooses the incorrect mode, mitigation techniques incorporate flick gestures and screen taps to correct or remove a stroke. An annotation environment was also developed to rapidly prototype annotation systems with user-supplied documents. Magic Pen was originally evaluated with 18 participants and further evaluated with 8 participants. Magic Pen was preferred over a more conventional menu approach, and using TL allowed for greater model predictability and stability. | en |
| dc.description.sponsorship | University of Ontario Institute of Technology | en |
| dc.identifier.uri | https://hdl.handle.net/10155/1492 | |
| dc.language.iso | en | en |
| dc.subject | Digital pen interfaces | en |
| dc.subject | Mode switching | en |
| dc.subject | Machine learning | en |
| dc.subject | Transfer learning | en |
| dc.subject | Error mitigation | en |
| dc.title | Magic Pen: automatic pen-mode switching for document annotation | en |
| dc.type | Thesis | en |