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Online writer identification system using adaptive sparse representation framework

Content Provider	IET Digital Library
Author	Venugopal, Vivek Sundaram, Suresh
Abstract	This study explores an adaptive sparse representation approach for online writer identification. The main focus is on employing prior information that quantifies the degree of importance of a dictionary atom concerning a given writer. This information is proposed by a fusion of two derived components. The first component is a saliency measure obtained from the sum-pooled sparse coefficients corresponding to the sub-strokes of a set of enrolled writers. The second component is a similarity score, computed for each dictionary atom with regards to a given writer, that is related to the reconstruction error of the sub-stroke based feature vectors. The proposed identification is accomplished with an ensemble of support vector machines (SVMs), wherein the input to the SVM trained for a writer is obtained by incorporating the adapted saliency values of that writer on the document descriptor obtained via average pooling of sparse codes. Experiments performed on the IAM and IBM-UB1 online handwriting databases demonstrate the efficacy of the proposed scheme.
Starting Page	126
Ending Page	133
Page Count	8
ISSN	20474938
Volume Number	9
e-ISSN	20474946
Issue Number	Issue 3, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/9/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2019.0147
Journal	IET Biometrics
Publisher Date	2020-02-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adapted Saliency Values Adaptive Sparse Representation Approach Adaptive Sparse Representation Framework Computer Vision And Image Processing Technique Derived Component Dictionary Atom Enrolled Writers Feature Extraction Given Writer Handwriting Recognition Handwritten Character Recognition Image Classification Image Recognition Image Representation Knowledge Engineering Technique Learning in AI Online Writer Identification System Saliency Measure Sparse Code Statistics Sub-stroke Based Feature Vector Sum-pooled Sparse Coefficient Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Signal Processing 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
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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