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Improving Online Handwriting Text/Non-text Classification Accuracy Under Condition of Stroke Context Absence

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11506))

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Abstract

In this paper, an approach to text/non-text stroke classification for on-line handwriting recognition is proposed. This approach allows to improve classification accuracy when the stroke’s context is absent. Having a label for the input stroke, we set this label for a feature vector which corresponds to each timestamp of the given input stroke. A trained neural network classifies each timestamp for the given input sequence. Finally, a decoder assigns a class label to the whole stroke. This approach was tested on the online handwritten dataset IAMonDO in on-line mode and has shown 98.5% accuracy. This approach could be used for other time-series classification tasks when the context information is not available.

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Correspondence to Serhii Polotskyi .

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Polotskyi, S., Deriuga, I., Ignatova, T., Melnyk, V., Azarov, H. (2019). Improving Online Handwriting Text/Non-text Classification Accuracy Under Condition of Stroke Context Absence. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_18

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20520-1

  • Online ISBN: 978-3-030-20521-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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