• DocumentCode
    118568
  • Title

    Khmer character recognition using artificial neural network

  • Author

    Hann Meng ; Morariu, Daniel

  • Author_Institution
    Fac. of Eng., Lucian Blaga Univ. of Sibiu, Sibiu, Romania
  • fYear
    2014
  • fDate
    9-12 Dec. 2014
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Character Recognition has become an interesting and a challenge topic research in the field of pattern recognition in recent decade. It has numerous applications including bank cheques, address sorting and conversion of handwritten or printed character into machine-readable form. Artificial neural network including self-organization map and multilayer perceptron network with the learning ability could offer the solution to character recognition problem. In this paper presents Khmer Character Recognition (KCR) system implemented in Matlab environment using artificial neural networks. The KCR system described the utilization of integrated self-organization map (SOM) network and multilayer perceptron (MLP) network with backpropagation learning algorithm for Khmer character recognition problem.
  • Keywords
    backpropagation; handwritten character recognition; mathematics computing; multilayer perceptrons; natural language processing; optical character recognition; self-organising feature maps; text detection; KCR system; Khmer character recognition system; MLP network; Matlab; address sorting; artificial neural network; backpropagation learning algorithm; bank cheques; handwritten character Recognition; integrated SOM network; integrated self-organization map network; machine-readable form; multilayer perceptron network; optical character recognition; pattern recognition; printed character Recognition; Artificial neural networks; Biological neural networks; Character recognition; Neurons; Noise; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA)
  • Conference_Location
    Siem Reap
  • Type

    conf

  • DOI
    10.1109/APSIPA.2014.7041824
  • Filename
    7041824