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Edge Based Graph Neural Network to Recognize Semigraph Representation of English Alphabets

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Mining Intelligence and Knowledge Exploration

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8284))

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Abstract

Graph Neural Network based on edges is introduced in this paper and is used to recognize the English uppercase alphabets treating their corresponding graphs as semigraphs. Graph Neural Network(GNN) is a connectionist model comprising of two feedforward neural networks (FNN) called transition network and output network connected by recurrent architecture according to the graph topology. The characteristics of the edges in a graph are considered as input for the transition network and the stabilized output of the transition network are taken as input for the output network. Edge based GNN is trained using error gradient method. Experimental results show that GNN is able to identify all the 26 graphs of alphabets correctly.

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Author information

Authors and Affiliations

  1. V.V. Vanniaperumal College for Women, Virudhunagar, 626001, Tamilnadu, India

    R. B. Gnana Jothi & S. M. Meena Rani

Authors
  1. R. B. Gnana Jothi
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  2. S. M. Meena Rani
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Editor information

Editors and Affiliations

  1. Department of Business Information Systems, FSIC, National University of Ireland, University College Cork, O’Rahilly Buildings, Cork, Ireland

    Rajendra Prasath

  2. Research Centre in Computer Science, V.H.N.Senthikumara Nadar College (Autonomous), 626 001, Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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© 2013 Springer International Publishing Switzerland

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Jothi, R.B.G., Rani, S.M.M. (2013). Edge Based Graph Neural Network to Recognize Semigraph Representation of English Alphabets. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_41

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  • DOI: https://doi.org/10.1007/978-3-319-03844-5_41

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03843-8

  • Online ISBN: 978-3-319-03844-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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