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Text/shape classifier for mobile applications with handwriting input

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Abstract

The paper provides a practical solution to a real-time text/shape differentiation problem for online handwriting input. The proposed structure of the classification system comprises stroke grouping and stroke classification blocks. A new set of features is derived that has low computational complexity. The method achieves 98.5 % text/shape classification accuracy on a benchmark dataset. The proposed stroke grouping machine learning approach improves classification robustness in relation to different input styles. In contrast to the threshold-based techniques, this grouping adaptation enhances the overall discriminating accuracy of the text/shape recognition system by 11.3 %. The solution improves system’s response on a touch-screen device.

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Authors and Affiliations

  1. Samsung R&D Institute Ukraine (SRK), 57, L’va Tolstogo Str., Kiev, 01032, Ukraine

    Illya Degtyarenko, Olga Radyvonenko, Kostiantyn Bokhan & Viacheslav Khomenko

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  1. Illya Degtyarenko
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  2. Olga Radyvonenko
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  3. Kostiantyn Bokhan
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  4. Viacheslav Khomenko
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Correspondence to Viacheslav Khomenko.

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Degtyarenko, I., Radyvonenko, O., Bokhan, K. et al. Text/shape classifier for mobile applications with handwriting input. IJDAR 19, 369–379 (2016). https://doi.org/10.1007/s10032-016-0276-0

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  • DOI: https://doi.org/10.1007/s10032-016-0276-0

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