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Online writer identification system using adaptive sparse representation framework

Publication Type : Journal Article

Publisher : IET Biometrics

Source : IET Biometrics, vol.9, no. 3, pp. 126 – 133

Campus : Bengaluru

School : School of Engineering

Department : Electronics and Communication

Year : 2020

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.

Cite this Research Publication : Vivek Venugopal and Suresh Sundaram, “ An Online Writer Identification System Using Adaptive Sparse Representation Framework", IET Biometrics, vol.9, no. 3, pp. 126 – 133

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