Classification of Historical Documents Based on LBP and LPQ Techniques
Pushpalata Gonasagi1, Shivanand S Rumma2, Mallikarjun Hangarge3
1Pushpalata Gonasagi, Department of PG Studies and Research in Computer Science, Gulbarga University, Kalaburagi, Karnataka State, India.
2Shivanand S Rumma, Department of PG Studies and Research in Computer Science, Gulbarga University, Kalaburagi, Karnataka State, India.
3Dr. Mallikarjun Hangrge*, Department f PG Studies and Research in Computer Science, Karnatak Arts, Science and Commerce College, Bidar, Karnataka State, India.
Manuscript received on December 11, 2019. | Revised Manuscript received on December 21, 2019. | Manuscript published on January 10, 2020. | PP: 1534-1539 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8579019320/2020©BEIESP | DOI: 10.35940/ijitee.C8579.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Historical documents are important source for knowing culture, language, social activities, educational system, etc. The historical documents are in different languages and evolved over centuries and transformed to present modern language, classification of documents into various eras, recognition of words etc. In this paper, we have proposed a new approach to automatic identification of the age of the historical handwritten document images based on LBP (Local Binary Pattern) and LPQ (Local Phase Quantization) algorithm. The standard historical handwritten document images named as MPS (Medieval Paleographic Scale) dataset which is publicly available is used to experiment. LBP and LPQ descriptors are used to extract the features of the historical document images. Further, documents are classified based on the discriminating feature values using classifiers namely K-NN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifier. The accuracy of historical handwritten document images by K-NN and SVM are 90.7% and 92.8% respectively.
Keywords: LBP, LPQ, K-NN, SVM, Document Age, Historical Document.
Scope of the Article: Classification