Unconstrained handwriting recoganization basedon neural network using connectionist temporal classification token passing algorithm

  • Authors

    • Pinagadi Venkateswararao
    • S Murugavalli
    2018-03-01
    https://doi.org/10.14419/ijet.v7i1.9.9825
  • Connectionist Temporat Classification Token Passing Calculation, Keyword Spotting, Handwriting Acknowledgment, Intermittent Neural System (RNN).

  • Abstract

    Recognition of human handwriting which offers the new way to improve the computer interface with the human and this process is very much useful for documents.Keyword spotting refers the spontaneous recognition of handwritten text, letter, and scripts from historical hand written books and the procedure of recovering all instance of a known keyword from an article. With a specific end goal to choose new components this paper, propose "a repetitive neural system manually written acknowledgment framework" for watchword spotting.

    The watchword seeing is finished utilizing an adjustment of the connectionist temporat classification Token Passing calculation in coincidence with a repetitive neural system. The proposed watchword spotting technique for written by hand message utilizing neural system, with another adaptation of connectionist temporat classification Token Pass calculation with quick and reliable catchphrase spotting can be executed without utilizing any content line or portioning separate words.

     

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  • How to Cite

    Venkateswararao, P., & Murugavalli, S. (2018). Unconstrained handwriting recoganization basedon neural network using connectionist temporal classification token passing algorithm. International Journal of Engineering & Technology, 7(1.9), 211-216. https://doi.org/10.14419/ijet.v7i1.9.9825

    Received date: 2018-03-03

    Accepted date: 2018-03-03

    Published date: 2018-03-01