DocumentCode
1580678
Title
Video-based on-line handwriting recognition
Author
Fink, Gernot A. ; Wienecke, Markus ; Sagerer, Gerhard
Author_Institution
Fac. of Technol., Bielefeld Univ., Germany
fYear
2001
fDate
6/23/1905 12:00:00 AM
Firstpage
226
Lastpage
230
Abstract
The use of handwriting provides a natural way of interacting with small portable computers. However, in order to capture handwritten text. online, special input devices are necessary. Therefore, M.E. Munich & P. Perona (1996) proposed to use visual input for pen-based computers. Writing can then be performed on ordinary paper, and pen trajectories are automatically extracted from image sequences recorded during the writing process. On the basis of this work, we developed a complete video-based online handwriting recognition system. We will present the techniques applied for pen tracking, pre-processing, feature extraction, and statistical modeling and recognition. Evaluation results on a writer-independent unconstrained handwriting recognition task demonstrate that the inherent limitations of the video-based approach can be compensated using robust modeling combined with adaptation techniques
Keywords
handwriting recognition; image sequences; notebook computers; online operation; tracking; video recording; video signal processing; adaptation techniques; feature extraction; handwritten text capture; image sequences; input devices; pen tracking; pen trajectory extraction; pen-based computers; portable computers; pre-processing; robust modeling; statistical modeling; video-based online handwriting recognition; visual input; writer-independent unconstrained handwriting recognition task; Adaptation model; Cameras; Feature extraction; Handwriting recognition; Image sequences; Personal digital assistants; Portable computers; Robustness; Spatial resolution; Writing;
fLanguage
English
Publisher
ieee
Conference_Titel
Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7695-1263-1
Type
conf
DOI
10.1109/ICDAR.2001.953788
Filename
953788
Link To Document