• DocumentCode
    2047094
  • Title

    α-Soft: An English Language OCR

  • Author

    Tariq, Junaid ; Nauman, Umar ; Naru, Muhammad Umair

  • Author_Institution
    Dept. of Comput. Sci., COMSATS Inst. of Inf. Technol., Islamabad, Pakistan
  • Volume
    1
  • fYear
    2010
  • fDate
    19-21 March 2010
  • Firstpage
    553
  • Lastpage
    556
  • Abstract
    Because of growing technology and fast living, business card are very much in demand. Most of the cards have fixed font size and style. That\´s why the OCR required for such documents does not need to be so expensive (computational cost) and complex (Artificial Neural Network). This paper presents a simple, efficient, and less costly approach to construct OCR for cards reading or any document that has fix font size and style. As English is an international language and almost every card have English characters on them. To achieve efficiency and less computational cost, OCR in this paper uses database instead of ANN (Artificial Neural Network) to recognize English characters which makes this OCR very simple to manage. As this paper is about English character recognition, so author is naming this OCR as α-soft, pronounced as "alpha soft". We have developed a prototype for this system. Different experiments are conducted to show that 100% accuracy is possible in OCR.
  • Keywords
    neural nets; optical character recognition; α-soft; ANN; English character recognition; OCR; artificial neural network; business card; Application software; Artificial neural networks; Character recognition; Computational efficiency; Computer applications; Computer science; Databases; Information technology; Natural languages; Optical character recognition software; DATABASE; ENGLISH CHARACTERS; OCR; OPTICAL CHARACTER RECOGNITION;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Engineering and Applications (ICCEA), 2010 Second International Conference on
  • Conference_Location
    Bali Island
  • Print_ISBN
    978-1-4244-6079-3
  • Electronic_ISBN
    978-1-4244-6080-9
  • Type

    conf

  • DOI
    10.1109/ICCEA.2010.112
  • Filename
    5445769