CFP last date
16 December 2024
Call for Paper
January Edition
IJAIS solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 16 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Evaluation of Classification and Feature Extraction Techniques for Simple Mathematical Equations

by Sanjay S. Gharde, Baviskar Pallavi V., K. P. Adhiya
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 5
Year of Publication: 2012
Authors: Sanjay S. Gharde, Baviskar Pallavi V., K. P. Adhiya
10.5120/ijais12-450183

Sanjay S. Gharde, Baviskar Pallavi V., K. P. Adhiya . Evaluation of Classification and Feature Extraction Techniques for Simple Mathematical Equations. International Journal of Applied Information Systems. 1, 5 ( February 2012), 34-38. DOI=10.5120/ijais12-450183

@article{ 10.5120/ijais12-450183,
author = { Sanjay S. Gharde, Baviskar Pallavi V., K. P. Adhiya },
title = { Evaluation of Classification and Feature Extraction Techniques for Simple Mathematical Equations },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2012 },
volume = { 1 },
number = { 5 },
month = { February },
year = { 2012 },
issn = { 2249-0868 },
pages = { 34-38 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number5/92-0183/ },
doi = { 10.5120/ijais12-450183 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:31.493986+05:30
%A Sanjay S. Gharde
%A Baviskar Pallavi V.
%A K. P. Adhiya
%T Evaluation of Classification and Feature Extraction Techniques for Simple Mathematical Equations
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 5
%P 34-38
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recognition of simple mathematical equation can applied on on-line or off-line samples. This system can applicable for publicly available dataset or researchers can prepare their own dataset for training and testing samples. In particular, we try to focus on evaluation of various methods used for recognition system. Moreover, some necessary issues in simple mathematical equation recognition will be addressed in depth. This paper discusses various steps of recognition process for simple mathematical equations. In that, pre-processing, segmentation, feature extraction, classification and recognition for mathematical symbol as well as for simple expression is described. Among the various phases applied in recognition system, features extraction and classification method may affect the overall accuracy of the system. Therefore, various techniques applied in this context are studied and comparative analysis is prepared. This evaluation study suggests better feature extraction and classification technique for improving the recognition rate of simple mathematical equation system.

References
  1. Francisco Álvaro, Joan Andreu Sanchez,” Comparing Several Techniques for Offline Recognition of Printed Mathematical symbols”, 2010 International Conference on Pattern Recognition.
  2. Qi Xiangweri Pan Yusup Wang Yang,” The study of structure analysis strategy in handwritten recognition of general mathematical expression”, 978-0-7695-3600-2/09 2009 IEEE.
  3. Nafiz Arica, “An Off-line character recognition system for free style Handwriting” thesis submitted on Sep 1998.
  4. J.Pradeep, E. Srinivasan, S.Himavathi, ”Neural Network based Handwritten Character Recognition system without feature extraction” International Conference on Computer, Communication and Electrical Technology 2011 IEEE.
  5. http://www.scribd.com/doc/60245721/English-Character-Recognition-System-Using-matlab.
  6. Nafiz Arica and Fatos T. Yarman-Vural, “An Overview of Character Recognition Focused on of Character Recognition Focused on Off-Line Handwriting” 2001 IEEE
  7. Shubhangi D.C., P.S. Hiremath, “Multi-Class SVM Classifier for English Handwritten Digit Recognition using Manual Class Segmentation” International Conference on Advances in Computing, Communication and Control, 2009 ACM
  8. Mou-Yen Chen, Amlan Kundu and Sargur N. Srihari, Fellow, “Variable Duration Hidden Markov Model and Morphological Segmentation for Handwritten Word Recognition”, IEEE transactions on image processing, vol. 4, no. 12, December 1995.
  9. Shailedra Kumar Shrivastava, Sanjay S. Gharde, “Support Vector Machine for Handwritten Devanagari Numeral Recognition” International Journal of Computer Applications (0975 – 8887), Volume 7– No.11, October 2010.
  10. Ahmad-Montaser Awal, Harold Mouchère, Christian Viard-Gaudin,” Towards Handwritten Mathematical Expression Recognition” 2009 10th International Conference on Document Analysis and Recognition.
  11. Dan cirasan, Dan pescaru,” Off-line Recognition of Handwritten Numeral Strings Composed from Two-digits Partially Overlapped Using Convolutional Neural Networks”, 978-1-4244-2673- 7/08 2008 IEEE.
  12. Stephen M. Watt and Xiao fang Xie, “Recognition for Large Sets of Handwritten Mathematical Symbols”, 1520-5263/05 2005 IEEE.
  13. Sajjad S. Ahranjany, Farbod Razzazi, Mohammad H. Ghassemian,” A Very High Accuracy Handwritten Character Recognition System for Farsi/Arabic Digits Using Convolutional Neural Networks”, 978-1-4244-6439-5/10 2010 IEEE.
  14. George Labahn, Edward Lank, Scott MacLean, Mirette Marzouk, David Tausky,” MathBrush: A System for doing Math on Pen-Based Devices”, 978-0-7695-3337-7/08 2008.
  15. Widad Jakjoud, Azzeddine Lazrek,” Segmentation method of offline Mathematical symbol”, 978-1-61284-732-0/11 2010 IEEE.
  16. Xue-dong Tian, Li-na Zuo, Fang Yang, Ming-hu Ha,” An Improved Method Based On Gabor Feature for Mathematical Symbol Recognition “, -4244-0973-X/07 2007 IEEE.
  17. Yu-sheng Guo, Lei Huang, Chang-ping Liu, Xin Jiang” An Automatic Mathematical Expression understanding System”, Institute of automation, Beijing 100080, china.
  18. Yu SHI and Frank K. SOONG “A symbol Graph Based Handwritten Math Expression Recognition “, 978-1-4244-2175-6/08 2008 IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Mathematical equation recognition; symbol recognition; support vector machine; segmentation; classification; feature extraction

Powered by PhDFocusTM