CFP last date
20 December 2024
Reseach Article

Character Recognition: A Neural Network Approach

Published on May 2012 by R. C. Tripathi, Vijay Kumar
National Conference on Advancement of Technologies – Information Systems and Computer Networks
Foundation of Computer Science USA
ISCON - Number 1
May 2012
Authors: R. C. Tripathi, Vijay Kumar
ec975262-d4ae-4298-85f4-37c584c8b1f2

R. C. Tripathi, Vijay Kumar . Character Recognition: A Neural Network Approach. National Conference on Advancement of Technologies – Information Systems and Computer Networks. ISCON, 1 (May 2012), 17-20.

@article{
author = { R. C. Tripathi, Vijay Kumar },
title = { Character Recognition: A Neural Network Approach },
journal = { National Conference on Advancement of Technologies – Information Systems and Computer Networks },
issue_date = { May 2012 },
volume = { ISCON },
number = { 1 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 17-20 },
numpages = 4,
url = { /proceedings/iscon/number1/6458-1005/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancement of Technologies – Information Systems and Computer Networks
%A R. C. Tripathi
%A Vijay Kumar
%T Character Recognition: A Neural Network Approach
%J National Conference on Advancement of Technologies – Information Systems and Computer Networks
%@ 0975-8887
%V ISCON
%N 1
%P 17-20
%D 2012
%I International Journal of Computer Applications
Abstract

OCR is the acronym for Optical Character Recognition. This technology allows a machine to automatically recognize characters through as optical mechanism. Human Beings "recognize" many objects in this manner; our eyes are the "optical mechanism. " But while the brain "sees" the input, the ability to comprehend these signals varies in each person according to many factors. In same manner "characters" which are nothing but the images made by the different combinations of lines and curves are also optically recognized by our brain. By reviewing these variables, the challenges faced by the technologist developing an OCR system. Character recognition techniques help in recognizing the characters written on paper documents and converting it in digital form. So Character recognition is gaining interest and importance in the modern world. While the area of character recognition is vast we focus on the fundamentals of character recognition, available techniques and emphasis on more recently used technique, neural networks. The paper throws light on, one of the application of Neural Network (NN) i. e. Character Recognition.

References
  1. Recognition of printed Assyrian character based on Neocognitron Artificial Neural Network by Nazar Saaid Sarhan, Laheeb AI-Zobady International Arab Journal of Information technology, Vol. 4, No. 1, January-2007
  2. Handwritten Character Recognition: A thesis submitted to the school of Information Technology and Electrical Engineering, the University of Queensland, By Migeul Po-Hsien Wu Visual Character recognition using artificial neural network, By Shashank Araokar.
  3. Kahan, S. T. , Pavlidis, T and Baird, "On recognition of Printed characters of any font and size", IEEE Transactions of pattern recognition and machine intelligence . PAM1-9, 1987, pp. 274-285.
  4. Hussain, B and Kabuka, M. R. , "A novel feature recognition neural network and its application to character recognition",IEEE Transactions of Pattem Recognition and Machine Intelligence,Vol. 16, No. 1,1994,pp. 98-106.
  5. Statistical Pattern Recognition: A Review By Anil K Jain, Robert P. W. Duin and Jainchang Mao, IEEE transaction on Pattern Analysis and Machine Intelligence, VOL. 22, No. 1, January 2000.
  6. An offline character recognition system for free style handwriting: A thesis submitted to the Graduate School of Natural and Applied Sciences of Middle East Technical University, By Nafiz Arica.
  7. Luger, G. F. , "Artificial Intelligence, Structures and Strategies for Complex Problem Solving", Addison-Wesley, 2005
  8. Demuth H. , Beale M. and Hagan M. (2006). Neural network toolbox for use with MATLAB. Neural Network Toolbox, IEE Savoy Place, London.
  9. Cognimem, CogniMem_1K: Neural network chip for high performance pattern recognition, datasheet, Version 1. 2. 1, www. recognetics. com, 2008.
  10. Xilinx, Inc. Xilinx Synthesis Technology (XST) User Guide. UG627 (v 11. 1. 0) www. xilinx. com, 2009.
  11. L. Leiva, M. Vázquez, N. Acosta, G. Sutter, "Herramienta de Generación de Arquitecturas Hardware para Reconocimiento de Patrones en Imágenes", JCRA 2007: Jornadas de Computación Reconfigurable y Aplicaciones. September 12-14, 2007, Zaragoza, España.
Index Terms

Computer Science
Information Sciences

Keywords

Ocr Nn Crs Knowledge-base