• DocumentCode
    2751999
  • Title

    An approach to multi-font numeral recognition

  • Author

    Arjun, N. Santosh ; Navaneetha, G. ; Preethi, G. Vishnu ; Babu, T. Karthik

  • Author_Institution
    Univ. Coll. of Eng. (A), Hyderabad
  • fYear
    2007
  • fDate
    Oct. 30 2007-Nov. 2 2007
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Recognition of numerals has been a research area for many years because of its various applications. But there wasn´t much research done for recognition of multi-font numerals. The approaches proposed so far, suffer from larger computation time and training for obtaining feature vectors. They can be extended to recognize many more fonts but the accuracy decreases rapidly. So as to eliminate these drawbacks, in this paper, we propose a method which recognizes 17 multi- fonts of different sizes varying from size 8 to 72, with an accuracy of 99.76% on a database of 2890 numeral images. The method requires less computation time for recognizing a numeral while maintaining the high amount of accuracy. In this method we use Euler number of a numeral to initially characterize the numbers into different groups. And then we use individual distinct features of each numeral for recognizing it.
  • Keywords
    character recognition; character sets; image recognition; vectors; Euler number; feature vectors; multifont numeral recognition; numeral images database; Character recognition; Educational institutions; Feature extraction; Genetic algorithms; Image databases; Image recognition; Multi-layer neural network; Neural networks; Optical character recognition software; Pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON 2007 - 2007 IEEE Region 10 Conference
  • Conference_Location
    Taipei
  • Print_ISBN
    978-1-4244-1272-3
  • Electronic_ISBN
    978-1-4244-1272-3
  • Type

    conf

  • DOI
    10.1109/TENCON.2007.4428888
  • Filename
    4428888