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Recognition of Multi-Stroke Based Online Handwritten Gurmukhi Aksharas

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Proceedings of the National Academy of Sciences, India Section A: Physical Sciences Aims and scope Submit manuscript

Abstract

In this paper, we have proposed Support Vector Machine (SVM) based efficient algorithm for Gurmukhi akshara formation/recognition. To train SVM classifier, a specific training data set has been used that forms an initial base to classify it. Experiment results have been obtained by using the software LibSVM where after preprocessing, coordinates were scaled from1 to 9 using the tool of LibSVM. In this experiment, 46,772 words were initially collected. In these words, 2,47,697 strokes were identified and further annotated. We tested the proposed methodology on 4,310 samples of Gurmukhi aksharas collected from different users. Testing results clearly reveal that for different combinations of Gurmukhi strokes and vowel/nasal, a high degree of accuracy has been achieved.

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Acknowledgments

We take this opportunity to thank Technology Development for Indian Languages (TDIL), DeitY, MoCIT, and Government of India for sponsoring the data collection used in this work.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Thapar University, Patiala, India

    Ravinder Kumar

  2. School of Mathematics and Computer Applications, Thapar University, Patiala, India

    Rajendra Kumar Sharma

  3. Department of Mathematics, Panjab University, Chandigarh, India

    Anuj Sharma

Authors
  1. Ravinder Kumar
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  2. Rajendra Kumar Sharma
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  3. Anuj Sharma
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Correspondence to Ravinder Kumar.

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Kumar, R., Sharma, R.K. & Sharma, A. Recognition of Multi-Stroke Based Online Handwritten Gurmukhi Aksharas. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 85, 159–168 (2015). https://doi.org/10.1007/s40010-014-0183-z

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  • DOI: https://doi.org/10.1007/s40010-014-0183-z

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