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Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features

Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features

Thippeswamy G., Chandrakala H. T.
Copyright: © 2020 |Volume: 9 |Issue: 3 |Pages: 15
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781799806677|DOI: 10.4018/IJNCR.2020070101
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MLA

Thippeswamy G., and Chandrakala H. T. "Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features." IJNCR vol.9, no.3 2020: pp.1-15. http://doi.org/10.4018/IJNCR.2020070101

APA

Thippeswamy G. & Chandrakala H. T. (2020). Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features. International Journal of Natural Computing Research (IJNCR), 9(3), 1-15. http://doi.org/10.4018/IJNCR.2020070101

Chicago

Thippeswamy G., and Chandrakala H. T. "Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features," International Journal of Natural Computing Research (IJNCR) 9, no.3: 1-15. http://doi.org/10.4018/IJNCR.2020070101

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Abstract

Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.

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