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Modified Sparse Representation Classification Framework for Online Writer Identification

Publication Type : Journal Article

Publisher : IEEE Trans. Systems

Source : IEEE Trans. Systems, Man, and Cybernetics: Systems, vol. 51, pp. 314-325

Url : https://ieeexplore.ieee.org/document/8488667

Campus : Bengaluru

School : School of Engineering

Department : Electronics and Communication

Year : 2021

Abstract : In this paper, we propose a modified strategy using sparse representation to identify the authorship of online handwritten documents. The note-worthy aspect of this paper lies in the introduction of a-priori information for each dictionary atom, that in a way indicates its degree of importance with respect to the dynamic characteristics of the writer. The methodology behind obtaining this information for the dictionary atoms is based on the computation of entropy values over histograms that are generated from the sparse coefficients during the training phase. The prelearned values are incorporated on the traditional schemes of “max” and “mean” pooling of sparse codes, thereby resulting in a modified writer descriptor. Experiments performed with the proposed sparse classification strategy on the handwritten samples of the IAM and IBM-UB1 online handwriting databases demonstrate promising results.

Cite this Research Publication : Vivek Venugopal and Suresh Sundaram, “A Modified Sparse Representation Classification Framework for Online Writer Identification," IEEE Trans. Systems,
Man, and Cybernetics: Systems, vol. 51, pp. 314-325

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