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Reseach Article

Segmentation of Offline Printed and Handwritten Mathematical Expressions

Published on August 2016 by Manisha Bharambe
National Conference on Digital Image and Signal Processing
Foundation of Computer Science USA
NCDISP2016 - Number 2
August 2016
Authors: Manisha Bharambe
40f0c581-a460-4a2a-9837-0d78c393d854

Manisha Bharambe . Segmentation of Offline Printed and Handwritten Mathematical Expressions. National Conference on Digital Image and Signal Processing. NCDISP2016, 2 (August 2016), 1-5.

@article{
author = { Manisha Bharambe },
title = { Segmentation of Offline Printed and Handwritten Mathematical Expressions },
journal = { National Conference on Digital Image and Signal Processing },
issue_date = { August 2016 },
volume = { NCDISP2016 },
number = { 2 },
month = { August },
year = { 2016 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/ncdisp2016/number2/25852-1635/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Digital Image and Signal Processing
%A Manisha Bharambe
%T Segmentation of Offline Printed and Handwritten Mathematical Expressions
%J National Conference on Digital Image and Signal Processing
%@ 0975-8887
%V NCDISP2016
%N 2
%P 1-5
%D 2016
%I International Journal of Computer Applications
Abstract

Mathematical expression recognition is an active research field and it becomes a challenging problem in the field of Optical character recognition. The fundamental problem of mathematical expression recognition system is the Off-line Printed expression recognition. One of the difficulties of handwritten mathematical symbol recognition lies in the variability of the symbols, different fonts in addition to the recognition of other language characters. The segmentation is the most important phase in the recognition of the expression. This paper deals with efficient segmentation technique to segment logical mathematical expressions with subscripts. In this paper, the database of 288 printed expressions and 960 handwritten expressions using logical symbols was developed. The proposed algorithm was tested on the handwritten and the printed expression database and the results are quite promising.

References
  1. Ahmad Moritaser Awal, Harold Mouchere, Christian Viard Gaudin. Towards Handwritten Mathematical Expression recognition IEEE 978-07095, 2009
  2. Ahmad Montaser Awal, Harold Mouchere, Christian Viard Gaudin. The problem of Handwritten mathematical expression recognition. ISBN, 978-0- 7695-4221-8,2010.
  3. Bharambe Manisha, "Recognition of Offline Handwritten Mathematical Expressions", International Journal of Computer Applications ISSN: 0975–8887,Vol. 108-No. 2 April 2014
  4. Bharambe Manisha, "Logical Symbol Recognition using Normalized Chain code and Density Features", International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 3, Issue-12, December 2014, pp: 619-62
  5. Hans Jurgen Winkler and Manfred Lang. On-Line Symbol segmentation and recognition inHandwritten mathematical expressions, 0-8186-7919-0/97,IEEE
  6. His-Jian Lee And J. Wang. Design of a mathematical expression recognition system, 0-8186-7128-9/95,IEEE
  7. Kang kim, Taik Rhee, Jae LEE. Utilizing consistency context for handwritten mathematical expression recognition. 978-0-7695-3725-2/2009 IEEE
  8. Kazuki Ashida, Masayuki Okamoto, Hiroki Imai, Performance Evaluation of a Mathematical Formula Recognition System with a large scale of printed formula images, Proceedings of the Second International Conference on Document Image Analysis for Libraries (DIAL'06),0-7695-2531- 8/06, 2006 IEEE
  9. Xue-Dong Tian, Hai-Yan Li, Xin-Fu Li. Research on symbol recognition for mathematical expressions. 0-7695- 2616-0/2006 ,IEEE.
  10. Xie,Xiaofang. On the recognition of handwritten mathematical symbols. Proquest NR39341,2008
  11. Francisco Álvaro, Richard Zanibbi, A Shape-Based Layout Descriptor for Classifying Spatial Relationships in Handwritten Math, 2013 ACM 978-1-4503-1789/4/13/09
  12. Qi Xiangwei Pan Weimin Yusup Wang Yang, The study of structure analysis strategy in handwritten recognition of general mathematical expression, International Forum on Information Technology and Applications, 978-0-7695-3600-2/09, 2009 IEEE
  13. Sanjay S. Garde, Pallavi V. Baviskar, K. P. Adhiya, Identification of Handwritten Simple Mathematical Equation Based on SVM and Projection Histogram, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-2, May 2013.
  14. Anita Jindal,Renu Dhir,Rajneesh Rani, Diagonal Features and SVM classifier for Handwritten Gurumukhi Character recognition, International Journal of Advance Reasearch in Computer science and software engineering, Vol 2, Issue 5, May 2012. ITRPPR, 2010.
  15. Taik HeonRhee, JinHyungKim , Efficient search strategy in structural analysis for handwritten mathematical expression recognition, patternrecognition (ScienceDirect)0031-32,2009 Elsevier
Index Terms

Computer Science
Information Sciences

Keywords

Optical Character Recognition Printed And Handwritten Logical Mathematical Expressions Segmentation.