Multi-Feature based Handwritten Script Identification at word level
Suryakanth Baburao Ummapure1, G. G. Rajput2

1Suryakanth Baburao Ummapure*, Department of Computer Science Gulbarga University, Kalaburagi, Karnataka, India.
2G G Rajput, Department of Computer Science Akkamahadevi Women’s University, Vijayapura 586106, Karnataka, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 3896-3901 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7772129219/2019©BEIESP | DOI: 10.35940/ijitee.B7772.129219
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Abstract: SIFT and LBP are two popular techniques used for obtaining “feature description” of the object. SIFT identifies key points that are locations with distinct image information and robust to scaling and rotation whereas, LBP transforms an image into an array of integer labels describing small scale appearance of the image. In this paper, we present an efficient method wherein “feature description” of handwritten document images at word level are computed using SIFT and LBP. Identification of script type is done using KNN and SVM classifiers. Experimental results show that the performance of SVM is better over KNN. Further, the proposed method is compared with other methods in the literature to demonstrate the efficacy of the proposed method. 
Keywords:  Script Identification, Word level, SIFTS, LBP, KNN, and SVM
Scope of the Article: Multi-Agent Systems