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Reduced Scattering Representation for Malayalam Character Recognition

  • Research Article - Special Issue - Computer Engineering and Computer Science
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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.

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Authors and Affiliations

  1. Center for Computational Engineering & Networking, Amrita University, Coimbatore, India

    K. Manjusha, M. Anand Kumar & K. P. Soman

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  1. K. Manjusha
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  2. M. Anand Kumar
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  3. K. P. Soman
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Correspondence to K. Manjusha.

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Manjusha, K., Anand Kumar, M. & Soman, K.P. Reduced Scattering Representation for Malayalam Character Recognition. Arab J Sci Eng 43, 4315–4326 (2018). https://doi.org/10.1007/s13369-017-2945-9

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  • DOI: https://doi.org/10.1007/s13369-017-2945-9

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