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Recognition of Semigraph Representation of Alphabets Using Edge Based Hybrid Neural Network

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Mining Intelligence and Knowledge Exploration (MIKE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9468))

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Abstract

Graph structured data are classified by connectionist models such as Graph neural network (GNN), recursive neural network. These models are based on the label of the nodes of the graph. An attempt has been made to consider the network based on edges. If a graph structured data is represented as semigraph, the number of edges will be reduced leading to a reduction in the number of networks in GNN. In this paper uppercase English alphabets represented as graphs are recognized using edge based hybrid neural network by viewing the graphs as semigraph. Experimental results show that the edge based hybrid neural network is able to identify all the graphs of alphabets correctly and outperforms edge based GNN.

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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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Corresponding author

Correspondence to R. B. Gnana Jothi .

Editor information

Editors and Affiliations

  1. Norwegian Univ. of Science & Technology, Trondheim, Norway

    Rajendra Prasath

  2. Intl Inst of Info Tech Hyderabad, Hyderabad, India

    Anil Kumar Vuppala

  3. V.H.N.S.N.College (Autonomous), Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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© 2015 Springer International Publishing Switzerland

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Jothi, R.B.G., Rani, S.M.M. (2015). Recognition of Semigraph Representation of Alphabets Using Edge Based Hybrid Neural Network. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26831-6

  • Online ISBN: 978-3-319-26832-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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