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Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network

by Sagar Shinde, Rajendra Waghulade
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 7
Year of Publication: 2016
Authors: Sagar Shinde, Rajendra Waghulade
10.5120/cae2016652125

Sagar Shinde, Rajendra Waghulade . Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network. Communications on Applied Electronics. 4, 7 ( March 2016), 1-6. DOI=10.5120/cae2016652125

@article{ 10.5120/cae2016652125,
author = { Sagar Shinde, Rajendra Waghulade },
title = { Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network },
journal = { Communications on Applied Electronics },
issue_date = { March 2016 },
volume = { 4 },
number = { 7 },
month = { March },
year = { 2016 },
issn = { 2394-4714 },
pages = { 1-6 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume4/number7/562-2016652125/ },
doi = { 10.5120/cae2016652125 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:54:14.150052+05:30
%A Sagar Shinde
%A Rajendra Waghulade
%T Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network
%J Communications on Applied Electronics
%@ 2394-4714
%V 4
%N 7
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwritten mathematical expressions recognition is yet challenging task due to its intricate spatial structure, tangled semantics and 2-dimensional layout of the characters. There is a still room for enhancement in recognition rate. Artificial neural network is superior to disentangle classification problems. In this paper, feed-forward back propagation neural network is implemented to achieve both character recognition and mathematical structure recognition with upgrade in effective performance in addition to accuracy of the experimental results including lessen efforts. System proves its potency by recognizing expressions in analysis of math documents.

References
  1. Ahmad-Montaser Awal, Harold Mouchere, Christian Viard-Gaudin, 2009, “Towards Handwritten Mathematical Expressions Recognition”, IEEE 10th International Conference on Document Analysis and Recognition, ICDAR 2009, Jul 2009, Barcelone, Spain. Pp.1046-1050, 2009.
  2. Dov Dori, David Doermann, Robert Haralick, Ihsin Phillips, Mitchell Buchman, David Ross. 1996, “ The representation of document structure: A generic object-process analysis”, Handbook on optical character recognition and document image analysis, World scientific publishing company, 1996.
  3. Wen-jiao Wang, Lei Han, Tian-yu Chen, Xue-dong Tian, 2007. “The Post-Processing for Mathematical Expressions Oriented on Integrative Rectification” ieeecomputersociety. pp: 894-897.
  4. DOROTHEA BLOSTEIN† and ANN GRBAVEC,1996, “ Recognition of mathematical notation”, Handbook on optical character recognition and document image analysis, World scientific publishing company, 1996
  5. Ming-Hu Ha , Xue-dong Tian , Na Li, 2006, “Structural Analysis of Printed Mathematical Expressions Based on Combined Strategy ” International Conference on Machine Learning and Cybernetics, Cybernetics, Dalian, China. pp 3354 – 3358, ISBN: 1-4244-0061-9."
  6. Tom M. Mitchell, 1997, International edit “ Machine Learning” McGraw-Hill Science/Engineering/Math ISBN:0070428077.
  7. Anurag Bhardwaj, Faisal Farooq, Huaigu Cao, Venu Govindaraju. 2008,” Topic based language models for OCR correction”, Proceedings of the second workshop on Analytics for noisy unstructured text data, New York, NY, USA , Pages 107-112, ISBN: 978-1-60558-196-5
  8. Dae Hwan Kim, Kim, J.H.. 2010, “Top-Down Search with Bottom-Up Evidence for Recognizing Handwritten Mathematical Expressions”, IEEE International Conference on Frontiers in Handwriting Recognition, Kolkata, pp 507 – 512, ISBN: 978-1-4244-8353-2.
  9. Al Hamad H.A, 8-10 Oct. 2013, “Use an efficient neural network to improve the Arabic handwriting recognition” , IEEE International Conference on Signal and Image Processing Applications, Melaka, pp 269 – 274, ISBN: 978-1-4799-0267-5.
  10. Xuedong Tian, Yan Zhang. July 30 2007-Aug.1 2007, “Segmentation of Touching Characters in Mathematical Expressions Using Contour Feature Technique “, IEEE Eighth ACIS International Conference on  Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. (SNPD- 2007), Qingdao, pp- 206 – 209, ISBN:978-0-7695-2909-7.
  11. Jakjoud, W, Lazrek, A., 7-9 April 2011, “Segmentation method of offline mathematical symbols”, IEEE International Conference on Multimedia Computing and Systems (ICMCS), Ouarzazate, pp. 1 – 7, ISBN: 978-1-61284-730-6.
  12. S. N. Sivanandam, S Sumathi, S. N Deepa , “ Introduction to neural network using MATLAB 6.0”, Tata Mcgraw- Hill Publishing company Limited,2006, ISBN : 0-07-059112-1.
  13. N. VenkateswaraRao, Dr. A. Srikrishna, Dr. B. RaveendraBabu, G. Rama Mohan Babu, October 2011, “ An efficient feature extraction and classification of handwritten digits using neural networks”, International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.5, DOI : 10.5121/ijcsea.2011.1505
Index Terms

Computer Science
Information Sciences

Keywords

Character recognition Math symbol recognition Handwritten math equations Feed forward back propagation neural network.

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