Evaluation of Fusion for Similarity Searching in Online Handwritten Documents

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.