Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
Information Retrieval
Indexing and Retrieval of On-line Handwritten Documents
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Biometric User Authentication for IT Security: From Fundamentals to Handwriting (Advances in Information Security)
Handwriting verification - Comparison of a multi-algorithmic and a multi-semantic approach
Image and Vision Computing
Machine learning in a multimedia document retrieval framework
IBM Systems Journal
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With the spread of TabletPCs handwriting raises in its significance and importance in the digital domain. Also there exist other devices with pen-based inputs like PDAs, digitizer tablets and pads specially prepared with sensors. The advantage of handwritten input methods is their possibility of an ad hoc creation of technical sketches and drawings alongside with text and that keyboards may be in some cases and environments bothersome. Therefore the amount of handwritten documents is likely to increase. But a great problem is a proper full text search on such documents. This paper discusses the effects of multi-sample and multi-algorithm fusion approaches, known from biometrics to increase the performance. The tests are done by using three different devices (Logitech ioPen, Pegasus PC NotesMaker, ACE CAD DigiMemo Digital) and five different feature extraction methods (square grid, triangular grid, slope, curvature and slant of writing) and show that fusion can improve the retrieval performance in terms of precision and recall from 0.903 and 0.935 without fusion to 0.958 and 0.943 with fusion, respectively.