NDLI: Feature combination for binary pattern classification

Content Provider	Springer Nature Link
Author	Hassan, Ehtesham Chaudhury, Santanu Gopal, M.
Copyright Year	2014
Abstract	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.
Starting Page	375
Ending Page	392
Page Count	18
File Format	PDF
ISSN	14332833
Journal	International Journal of Document Analysis and Recognition (IJDAR)
Volume Number	17
Issue Number	4
e-ISSN	14332825
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-06-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Feature combination Character recognition Symbol recognition Multiple kernel learning Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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