• DocumentCode
    3579306
  • Title

    A neural network based handwritten Meitei Mayek alphabet optical character recognition system

  • Author

    Laishram, Romesh ; Singh, Pheiroijam Bebison ; Singh, Thokchom Suka Deba ; Anilkumar, Sapam ; Singh, Angom Umakanta

  • Author_Institution
    Electronics & Communication Engineering, Manipur Institute of Technology, Imphal, India
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Handwritten character recognition is a part of optical character (OCR) system. OCR can be applied to both printed text and handwritten documents. In this paper we discussed the handwritten character recognition of Meitei Mayek (Manipuri script). Although OCR has been studied and developed for many Indian script very few works have been reported so far for Meitei-Mayek. This paper describes the handwritten Meitei Mayek (Manipuri script) alphabets recognition (HMMAR) using a neural network approach. The alphabet database is pre-processed and the extracted feature is sent to a neural network system for training. The trained neural network is further tested and performance analysis is observed. The emphasis is given on the process of character segmentation from a whole document i.e. isolating a single character image from a complete scanned document.
  • Keywords
    Artificial neural networks; Character recognition; Handwriting recognition; Histograms; Image segmentation; Optical character recognition software; Histogram; Meitei-Mayek; Neural Network; OCR; Segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on
  • Print_ISBN
    978-1-4799-3974-9
  • Type

    conf

  • DOI
    10.1109/ICCIC.2014.7238510
  • Filename
    7238510