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Feature combination for binary pattern classification
Hassan E., Chaudhury S.,
Published in Springer Verlag
Volume: 17
Issue: 4
Pages: 375 - 392
The paper presents a novel framework for large class, binary pattern classification problem by learning-based combination of multiple features. In particular, class of binary patterns including characters/primitives and symbols has been considered in the scope of this work. We demonstrate novel binary multiple kernel learning-based classification architecture for applications including such problems for fast and efficient performance. The character/primitive classification problem primarily concentrates on Gujarati and Bangla character recognition from the analytical and experimental context. A novel feature representation scheme for symbols images is introduced containing the necessary elastic and non-elastic deformation invariance properties. The experimental efficacy of proposed framework for symbol classification has been demonstrated on two public data sets. © 2014, Springer-Verlag Berlin Heidelberg.
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Published in Springer Verlag
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