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Reduced Scattering Representation for Malayalam Character Recognition

Publication Type : Journal Article

Publisher : Volume 43,

Source : Volume 43, Issue 8, p.4315 - 4326 (2018)

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Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)

Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication

Year : 2018

Abstract : Scattering convolution network generates stable feature representation by applying a sequence wavelet decomposition operation on input signals. The feature representation in higher layers of the network builds a large-dimensional feature vector, which is often undesirable in most of the applications. Dimension reduction techniques can be applied on these higher-dimensional feature descriptors to produce an informative representation. In this paper, singular value decomposition is applied to the higher-layer scattering representation to generate informative feature descriptors. The effectiveness of the reduced scattering representation is evaluated on Malayalam printed and handwritten character recognition using support vector machine classifier. The reduced scattering representation improves the recognition performance when combining with lower-layer scattering network features.

Cite this Research Publication : K. Manjusha, M. Kumar, A., and Dr. Soman K. P., “Reduced Scattering Representation for Malayalam Character Recognition”, vol. 43, no. 8, pp. 4315 - 4326, 2018.

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