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    Rata.SSR: Data Mining for Pertinent Stroke Recognizers

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    Date
    2010
    Author
    Chang, Samuel Hsiao-Heng
    Plimmer, Beryl
    Blagojevic, Rachel
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    Abstract
    While many approaches to digital ink recognition have been proposed, most lack flexibility and adaptability to provide acceptable recognition rates across a variety of problem spaces. Time and expert knowledge are required to build accurate recognizers for a new domain. This project uses selected algorithms from a data mining toolkit and a large feature library, to compose a tailored software component (Rata.SSR) that enables single stroke recognizer generation from a few example diagrams. We evaluated Rata.SSR against four popular recognizers with three data sets (one of our own and two from other projects). The results show that it outperforms other recognizers on all tests except recognizer and data set pairs (e.g. PaleoSketch recognizer and PaleoSketch data set) in these cases the difference is small, and Rata is more flexible. We hence demonstrate a flexible and adaptable procedure for adopting existing techniques to quickly generate accurate recognizers without extensive knowledge of either AI or data mining.
    BibTeX
    @inproceedings {10.2312:SBM:SBM10:095-102,
    booktitle = {Eurographics Workshop on Sketch-Based Interfaces and Modeling},
    editor = {Marc Alexa and Ellen Yi-Luen Do},
    title = {{Rata.SSR: Data Mining for Pertinent Stroke Recognizers}},
    author = {Chang, Samuel Hsiao-Heng and Plimmer, Beryl and Blagojevic, Rachel},
    year = {2010},
    publisher = {The Eurographics Association},
    ISSN = {1812-3503},
    ISBN = {978-3-905674-25-5},
    DOI = {10.2312/SBM/SBM10/095-102}
    }
    URI
    http://dx.doi.org/10.2312/SBM/SBM10/095-102
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    Eurographics Association copyright © 2013 - 2022 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA