Identification and classification of historical Kannada handwritten document images using LBP features
Parashuram Bannigidad, Chandrashekar Gudada · International Journal of Intelligent Systems Design and Computing · 2018
Digitisation process is very much essential for historical handwritten documents due to degraded qualities in the manuscript such as ink bleed, arbitrary geometric distortions, storage conditions and age. In this work, the age-type identification and classification of historical Kannada handwritten document images is done by applying text-block wise segmentation method, extracting the LBP features and using LDA, K-NN and SVM classifiers. The purpose of the present work is to identify the document script of the dynasties, whether it belongs to Hoysala dynasty or Vijayanagara dynasty or Mysore Wodeyar's dynasty? The average classification accuracy of the proposed method with LDA classifier is 94.6%, whereas K-NN classifier yielded 98.3% and SVM classifier yielded 99.3%. As per the results obtained from the experimentation, it is proved that the SVM classifier has got good classification ability comparatively with respect to LDA and K-NN classifiers for historical Kannada handwritten document images of all age-type scripts.