Online Handwritten Gurmukhi Character Recognition using Hybrid Feature Set

  • Authors

    • Mandeep Singh
    • Karun Verma
    • Bob Gill
    • Ramandeep Kaur
    2018-06-25
    https://doi.org/10.14419/ijet.v7i3.4.16753
  • Feature extraction, Gurmukhi script, K-nearest neighbor, online recognition, Support Vector Machines.

  • Abstract

    Online handwriting character recognition is gaining attention from the researchers across the world because with the advent of touch based devices, a more natural way of communication is being explored. Stroke based online recognition system is proposed in this paper for a very complex Gurmukhi script. In this effort, recognition for 35 basic characters of Gurmukhi script has been implemented on the dataset of 2019 Gurmukhi samples. For this purpose, 32 stroke classes have been considered. Three types of features have been extracted. Hybrid of these features has been proposed in this paper to train the classification models. For stroke classification, three different classifiers namely, KNN, MLP and SVM are used and compared to evaluate the effectiveness of these models. A very promising “stroke recognition rate†of 94% by KNN, 95.04% by MLP and 95.04% by SVM has been obtained.

     

     

  • References

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  • How to Cite

    Singh, M., Verma, K., Gill, B., & Kaur, R. (2018). Online Handwritten Gurmukhi Character Recognition using Hybrid Feature Set. International Journal of Engineering & Technology, 7(3.4), 90-95. https://doi.org/10.14419/ijet.v7i3.4.16753

    Received date: 2018-08-03

    Accepted date: 2018-08-03

    Published date: 2018-06-25