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Parashuram Bannigidada
Chandrashekar Gudada


Our ancestor tried in a many ways to transfer their heritage by inscribing on stone, wooden boards, leafs, metal plates cloths and lastly, on paper, which tends to a long lasting materials. These inscriptions usually contains information related to our civilization, religion, science, astrology, education, etc., and most of these inscriptions are degraded because of its aging, ink bleed-through, folding, etc., due to this degradness, the documents are not possible to read and understand the contents. Hence, it is very much essential to restore by digitising the historical Kannada handwritten documents and also recognise the originality of dynasty it belongs. The main objective of the present study is to digitize, restore and identify the historical Kannada handwritten document images by applying image enhancement techniques; and block wise segmentation method. The identification and classification is done by extracting GLCM features and LDA, K-nearest neighbour (K-NN) and SVM classifiers. In this paper, we have considered historical Kannada handwritten document images of different dynasties based on their age-type; Vijayanagara dynasty (1460 AD), Mysore Wodeyars dynasty (1936 AD), Vijayanagara dynasty (1400 AD) and Hoysala dynasty (1340 AD) for experimentation. The average classification accuracy for different dynasties are; The LDA classifier has yielded an accuracy of 88.2%, K-NN classifier has got 92.3% and SVM classifier has 96.7%, It is observed that, the SVM classifier has got a good classification performance comparatively LDA and K-NN classifiers for Historical Kannada handwritten document images. The results are also compared with manual results obtained by the Epigraphists and language experts, which demonstrate the efficacy and exhaustiveness of the proposed method.


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Author Biographies

Parashuram Bannigidada, Rani Channamma University, Belagavi

Department of Computer Science, Assistant Professor

Chandrashekar Gudada, Rani Channamma University, Belagavi

Department of Computer Science, Research Scholar


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