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Handwritten Recognition using Slope and Curvature Functions

by Mehdi Yaghoubi, Soheila Karbasi
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 8
Year of Publication: 2012
Authors: Mehdi Yaghoubi, Soheila Karbasi
10.5120/ijais12-450798

Mehdi Yaghoubi, Soheila Karbasi . Handwritten Recognition using Slope and Curvature Functions. International Journal of Applied Information Systems. 4, 8 ( December 2012), 17-20. DOI=10.5120/ijais12-450798

@article{ 10.5120/ijais12-450798,
author = { Mehdi Yaghoubi, Soheila Karbasi },
title = { Handwritten Recognition using Slope and Curvature Functions },
journal = { International Journal of Applied Information Systems },
issue_date = { December 2012 },
volume = { 4 },
number = { 8 },
month = { December },
year = { 2012 },
issn = { 2249-0868 },
pages = { 17-20 },
numpages = {9},
url = { https://www.ijais.org/archives/volume4/number8/378-0798/ },
doi = { 10.5120/ijais12-450798 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:47:49.325426+05:30
%A Mehdi Yaghoubi
%A Soheila Karbasi
%T Handwritten Recognition using Slope and Curvature Functions
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 4
%N 8
%P 17-20
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Letter recognition and handwritten processing is one of the major and open problems in Artificial Intelligent (AI) domain. This study introduces a method based on statistical and geometrical techniques to recognize handwritten digits and letters. These techniques use the fuzzy logic to create the vector curves. Inputs are online digits or letters and outputs are two arrays of slope and curvature values. The slope and curvature values of training data are stored in a database and used in comparison phase. The test results show that 96. 98% of inputs are correctly recognized.

References
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  2. R. C. Gonzalez and P. Wints, "Digital Image Processing", Addison-Wesley pp. 399-402, 1987.
  3. H. Ishibuchi, K. Nozaki and H. Tanaka, "Distributed Representation of Fuzzy Rules and its Application to Pattern Classification", Fuzzy Set and System, Vol. 52, pp. 21-32, 1992.
  4. H. Khosravi and E. Kabir, "Introducing a Very Large dataset of handwritten Farsi digits and an study on their varieties" Center of Pattern Recognition Letters Vol. 28, pp. 1133-1141, 2007.
  5. C. L. Liu, et al,"Handwritten Digit Recognition: Benchmarking of State-of-the-art Techniques", Pattern Recognition, pp. 2271-2285, 2003.
  6. M. shi et al,"Handwritten Numeral Recognition Using Gradient and Curvature of Gray scale Image", Pattern Recognition, pp. 2051-2059, 2002.
  7. P. siy and C. S. Chen "Fuzzy Logic for Handwritten Numerical Characters Recognition", IEEE Trans. Sys. Man. Cybern, pp. 570-575, 1974.
Index Terms

Computer Science
Information Sciences

Keywords

Vector curve Slope function Curvature function

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