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Reseach Article

Diagnosis of Mathematical Symbols using Hidden Markov Model

by Mohamad Hassan Asadi, Abbas Akkasi, Ebrahim Zargarpour, Zahra Mohammdi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 1
Year of Publication: 2015
Authors: Mohamad Hassan Asadi, Abbas Akkasi, Ebrahim Zargarpour, Zahra Mohammdi
10.5120/ijca2015905711

Mohamad Hassan Asadi, Abbas Akkasi, Ebrahim Zargarpour, Zahra Mohammdi . Diagnosis of Mathematical Symbols using Hidden Markov Model. International Journal of Computer Applications. 125, 1 ( September 2015), 40-42. DOI=10.5120/ijca2015905711

@article{ 10.5120/ijca2015905711,
author = { Mohamad Hassan Asadi, Abbas Akkasi, Ebrahim Zargarpour, Zahra Mohammdi },
title = { Diagnosis of Mathematical Symbols using Hidden Markov Model },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 1 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 40-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number1/22399-2015905711/ },
doi = { 10.5120/ijca2015905711 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:54.213219+05:30
%A Mohamad Hassan Asadi
%A Abbas Akkasi
%A Ebrahim Zargarpour
%A Zahra Mohammdi
%T Diagnosis of Mathematical Symbols using Hidden Markov Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 1
%P 40-42
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diagnosis of mathematical symbols in handwritings is originated from Optical Character Recognition (OCR) method. Recognition of mathematical symbols increases the accuracy of calculations. In present study, hidden Markov model is applied with a new feature selection system. Considering previous studies, a lot of researches performed on mathematical symbols recognition, have used support vector machine. Test process in this method is time-consuming and it is not advised to use it. In this new approach, the result is 96.05% accuracy for Infity database and 96% for IRISA database.

References
  1. Sameh M. Awaidah and other. A multiple feature/resolution scheme to Arabic (Indian) numerals recognition using hidden Markov models.signal processing, In press, 2009.
  2. C.-L.Liu and C.Y.Suen. A new benchmark on the recognition of handwritten bangla and farsi numeral characters.Pattern Recognition, In press, 2008.
  3. W.M. Pan, T.D. Bui, and C.Y. Suen : Isolated Handwritten Farsi numerals Recognition Using Sparse And Over-Complete Representations, 2009 10th International Conference on Document Analysis and Recognition
  4. Yasemin Altun and Ioannis Tsochantaridis and Thomas Hofmann: Hidden Markov Support Vector Machines, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.
  5. Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006 Springer ScienceBusiness Media, LLC
  6. Usama Fayyad: A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 2, 121–167 (1998)
  7. Ahmad A.R, Viard-Gaudin, C. Khalid M: Lexicon-based Word Recognition Using Support Vector Machine and Hidden Markov Model, 2009 10th International Conference on Document Analysis and Recognition
  8. F. Solimanpour, J. Sadri, C.Y. Suen, Standard databases for recognition of handwritten digits, numerical strings, legal amounts, letters and dates in Farsi language, in: Proceedings of the 10th International Workshop on Frontiers of Handwriting Recognition, La Baule, France, 2006, pp. 3–7.
  9. Al-Omari, F., Al-Jarrah, O.: Handwritten Indian numerals recognition system using probabilistic neural networks. Adv. Eng. Inform. 18(1), 9–16 (2004)
  10. Said, F., Yacoub, R., Suen, C.: Recognition of English and Arabic numerals using a dynamic number of hidden neurons. Proc. 5th ICDAR, pp. 237–240, 1999
  11. H. Drucker, B. Shahrary, D.C. Gibbon, “Support vector machines: relevance feedback and information retrieval” ,Information Processing and Management 38, p305-323, 2002
  12. Sherif Abdleazeem and Ezzat El-Sherif: Arabic handwritten digit recognition, IJDAR (2008) 11:127–141
  13. Birendra Keshari and Stephen M. Watt, Hybrid Mathematical Symbol Recognition using Support Vector Machines, IJDAR (2007)
  14. Christopher Malon and etc, Mathematical symbol recognition with support vector machines, Pattern Recognition Letters 29 (2008)
  15. Jason Ranger and etc, Optical Character Recognition of Printed Mathematical Symbols using A Hierarchical Classifier, IPCV (2012)
  16. Abdullah Almaksour and etc, Optical Personalizable Pen-Based Interface Using Life-Long Learning, International Conference on Frontiers in Handwriting Recognition (ICFHR), Aug 2010, Kolkata, India. pp.188-193, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Mathematical symbols optical character recognition (OCR) hidden Markov model