Open AccessSIFT and HOG features for the retrieval of ancient Kannada epigraphs
Author(s) -
Parashivamurthy Ravi,
Naveena Chikkaguddaiah,
Sharath Kumar Yeliyur Hanumathiah
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0715
Subject(s) - scale invariant feature transform , search engine indexing , principal component analysis , pattern recognition (psychology) , artificial intelligence , histogram , computer science , histogram of oriented gradients , tree (set theory) , feature extraction , feature (linguistics) , mathematics , image (mathematics) , mathematical analysis , linguistics , philosophy
In this work, the authors presented the indexing method for efficient retrieval of Kannada characters. Here the characters were segmented by the connected component method. Then features were extracted by scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) methods. These features were condensed by principal component analysis. They presented the indexing approach using K ‐dimensional tree (K‐D tree) to improve the identification process. For the experiment, they used their own database. The results of the experiment show that the indexing prior is faster than conventional identification approaches in terms of time to script. From experimentation, they observe that fusion features achieve the maximum accuracy of 90% with varying principal component analysis features.