Abstract
A character is the smallest unit in every line of a script, the development of the script stretches over millions of inscriptions in Kannada, and these inscriptions play a vital role in the reconstruction of Karnataka’s history and its culture. The Kannada script has evolved for more than 1500 years and many inventions, and variations have taken place in the scripts used by various dynasties. Due to these variations occurred in the script, it has been improved in text and formed in a new different style. The objective of the present paper is to digitize and restore the historical Kannada handwritten scripts by applying image enhancement techniques and recognize the individual characters by extracting the HOG features. The LDA, K-nearest neighbour (K-NN), and SVM classifiers are used to identify the dynasties of the characters, whether it belongs to the dynasty of the Hoysala or Vijayanagara or the Mysore Wodeyar. The average classification accuracy of the historical Kannada handwritten characters from the different regimes is; the LDA classifier has yielded 68.4%, the K-NN classifier achieved 85.7%, and the SVM classifier yielded 87.5%. Based on the experimentation, it is noted that the overall classification accuracy is relatively improved with the SVM classifier compared to LDA and K-NN classifiers. Further, the results are also verified manually obtained by epigraphists and linguistic experts, which proves the efficacy of the proposed approach.
The authors are grateful to the authorities of the Rani Channamma University, Belagavi, Karnataka, India for sanctioning minor research project and provided financial assistance to carry out this research work (RCUB/PMEB/2020-21/37 Dt. 15.05.2020).
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Bannigidad, P., Gudada, C. (2021). Historical Kannada Handwritten Character Recognition Using Machine Learning Algorithm. In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_30
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DOI: https://doi.org/10.1007/978-3-030-73689-7_30
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