Title: Identification and classification of historical Kannada handwritten document images using LBP features
Authors: Parashuram Bannigidad; Chandrashekar Gudada
Addresses: Department of Computer Science, Rani Channamma University, Belagavi, 591 156, Karnataka, India ' Department of Computer Science, Rani Channamma University, Belagavi, 591 156, Karnataka, India
Abstract: 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.
Keywords: manuscripts; Kannada; handwritten documents; LBP features; historical documents; Hastaprati; identification; classification; segmentation; SVM; KNN; linear discriminant analysis; LDA; archival; dynasty.
International Journal of Intelligent Systems Design and Computing, 2018 Vol.2 No.2, pp.176 - 188
Available online: 20 Nov 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article