Title
Estimating Writing Neatness From Online Handwritten Data
Abstract
Handwriting is the most fundamental expressive activity in learning. To utilize the intuitiveness and the nature of handwriting, digital pen technology has emerged to capture and transfer notes. We developed AirTransNote, a student note-sharing system that facilitates collaborative and interactive learning in conventional classrooms. A teacher can use the AirTransNote system to share student notes with the class on a projected screen immediately to enhance the group learning experience. However, to improve the effectiveness of sharing notes, the teacher must be able to select an effective note for sharing. This can be difficult and time consuming during a lecture. Moreover, students should be encouraged to improve the presentation of their handwritten notes. Well-written notes are more accessible for other students and reduce irrelevant and careless mistakes. To facilitate learning improvements based on note sharing, we require a method to estimate the neatness of a note automatically. If a method is established, the teacher can easily select effective notes. Furthermore, this method can help provide feedback to the student to improve their writing. We examined 14 basic features from handwritten notes by considering correlation coefficients and found that the variance of pen speed, angular point average, and pen speed average were the significant features for evaluating the neatness of handwritten notes.
Year
DOI
Venue
2014
10.20965/jaciii.2014.p0946
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
Keywords
DocType
Volume
consciousness, carefulness, improved handwriting, learning attitudes, anoto digital pen
Journal
18
Issue
ISSN
Citations 
6
1343-0130
2
PageRank 
References 
Authors
0.38
4
2
Name
Order
Citations
PageRank
Motoki Miura18721.85
Takamichi Toda220.38