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Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure

G. Appa Rao1 , G. Srinivas2 , K.Venkata Rao3 , P.V.G.D. Prasad Reddy4

  1. Department of CSE, GIT, GITAM,VISAKHAPATNAM,INDIA.
  2. Department of IT, ANITS, VISAKHAPATNAM, INDIA.
  3. Department of CSSE, AndhraUniversity,VISAKHAPATNAM,INDIA.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-4 , Page no. 400-404, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i4.400404

Online published on Apr 30, 2018

Copyright © G. Appa Rao, G. Srinivas, K.Venkata Rao, P.V.G.D. Prasad Reddy . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: G. Appa Rao, G. Srinivas, K.Venkata Rao, P.V.G.D. Prasad Reddy, “Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.400-404, 2018.

MLA Style Citation: G. Appa Rao, G. Srinivas, K.Venkata Rao, P.V.G.D. Prasad Reddy "Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure." International Journal of Computer Sciences and Engineering 6.4 (2018): 400-404.

APA Style Citation: G. Appa Rao, G. Srinivas, K.Venkata Rao, P.V.G.D. Prasad Reddy, (2018). Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure. International Journal of Computer Sciences and Engineering, 6(4), 400-404.

BibTex Style Citation:
@article{Rao_2018,
author = {G. Appa Rao, G. Srinivas, K.Venkata Rao, P.V.G.D. Prasad Reddy},
title = {Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2018},
volume = {6},
Issue = {4},
month = {4},
year = {2018},
issn = {2347-2693},
pages = {400-404},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1909},
doi = {https://doi.org/10.26438/ijcse/v6i4.400404}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i4.400404}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1909
TI - Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure
T2 - International Journal of Computer Sciences and Engineering
AU - G. Appa Rao, G. Srinivas, K.Venkata Rao, P.V.G.D. Prasad Reddy
PY - 2018
DA - 2018/04/30
PB - IJCSE, Indore, INDIA
SP - 400-404
IS - 4
VL - 6
SN - 2347-2693
ER -

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Abstract

The key predicament in the present circumstances is how to categorize the mathematically related keywords from a given text file and store them in one math text file. As the math text file contains only the keywords which are related to mathematics. The math dataset is a collection of huge amount of tested documents and stored in math text file. The dataset is trained with giant amount of text files and the size of dataset increases, training with various text samples. Finally the dataset contains only math-related keywords. The proposed approaches evaluated on the text containing individual formulas and repeated formulas. The two approaches proposed are one is Sequence matcher and another one is Levenshtein Distance, both are used for checking string similarity. The performance of the repossession is premeditated based on dataset of repetitive formulas and formulas appearing once and the time taken for reclamation is also measured.

Key-Words / Index Term

Levenshtein distance,Sequence matcher

References

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