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Reseach Article

Handwritten Gregg Shorthand Recognition

by R. Rajasekaran, K. Ramar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 9
Year of Publication: 2012
Authors: R. Rajasekaran, K. Ramar
10.5120/5572-7666

R. Rajasekaran, K. Ramar . Handwritten Gregg Shorthand Recognition. International Journal of Computer Applications. 41, 9 ( March 2012), 31-38. DOI=10.5120/5572-7666

@article{ 10.5120/5572-7666,
author = { R. Rajasekaran, K. Ramar },
title = { Handwritten Gregg Shorthand Recognition },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 9 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number9/5572-7666/ },
doi = { 10.5120/5572-7666 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:29:11.947971+05:30
%A R. Rajasekaran
%A K. Ramar
%T Handwritten Gregg Shorthand Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 9
%P 31-38
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gregg shorthand is a form of stenography that was invented by John Robert Gregg in 1888. Like cursive longhand, it is completely based on elliptical figures and lines that bisect them. Gregg shorthand is the most popular form of pen stenography in the United States and its Spanish adaptation is fairly popular in Latin America. With the invention of dictation machines, shorthand machines, and the practice of executives writing their own letters on their personal computers, the use of shorthand has gradually declined in the business and reporting world. However, Gregg shorthand is still in use today. The need to process documents on paper by computer has led to an area of research that may be referred to as Document Image Understanding (DIU). The goal of DIU system is to convert a raster image representation of a document. For Example, A hand written Gregg shorthand character or word is converted into an appropriate Symbolic Character in a computer (It may be a scanned character or online written character). Thus it involves many disciplines of computer science including image processing, pattern recognition, natural language processing, artificial intelligence, neural networks and database system. The ultimate goal of this paper is to recognize hand written Gregg shorthand character and Gregg shorthand word.

References
  1. About Gregg shorthand's history and world records http://gregg. angelfishy. net/anaboutg. shtml
  2. Leedham C G, Downtown A C 1984 On-line recognition of short forms in Pitmans' handwritten shorthand. Proc. 7th Int. Conf. on Pattern Recognition, Montreal, pp 2. 1058–2. 1060
  3. Leedham C G, Downtown A C 1986 On-line recognition of Pitmans' handwritten shorthand – An evaluation potential. Int. J. Man-Machine Studies 24: 375–393
  4. Leedham C G, Downtown A C 1987 Automatic recognition and transcription of Pitman's handwritten shorthand - An approach to short forms. Pattern Recogn. 20: 341–348
  5. Leedham C G, Downtown A C 1990 Automatic recognition and transcription of Pitman's handwritten shorthand. Computer processing of handwriting (eds) R Plamondon, C G Leedham (Singapore:World Scientific)
  6. Leedham C G, Downtown A C, Brooks C P, Newell A F 1984 On-line acquisition of Pitman's handwritten shorthand as a means of rapid data entry. Proc. Int. Conf. on Human-Computer Interaction,
  7. Hemanth Kumar G 1998 Automation of text production from Pitman shorthand notes. Ph D thesis,University of Mysore, Mysore
  8. Nagabhushan P, Anami B S 1999 A knowledge based approach for composing English text fromphonetic text documented through Pitman shorthand language. Int. Conf. On Computer Science (ICCS-99), New Delhi, pp 318–327
  9. Nagabhushan P, Anami B S 2000 A knowledge based approach for recognition of grammalogues and punctuation symbols useful in automatic English text generation from Pitman shorthand language documents. Proc. Natl. Conf. on Recent Trends in Advanced Computing (NCRTAC-2000),Thirunelveli, pp 175–183
  10. Nagabhushan P, Murli 2000 Tangent feature values and cornarity index to recognise handwritten PSL words. Proc. Natl. Conf. on Document Analysis and Recognition (NCDAR), Mandya, India,pp 49–56
  11. Nagabhushan P, Anami A knowledge-based approach for recognition of handwritten Pitman shorthand language strokes
  12. Rahul kala, Harsh Vazirani, Anupam Shukla and Ritu Tiwari offline character recognition using Genitic Algorithm IJCSI International Journal of Computer Science Issues, March 2010 pp 16-25
  13. Shashank Araokar Visual Character Recognition using Artificial Neural Networks
  14. Anil K. Jain, Jianchang Mao, K. M. Mohiuddin, Artificial Neural Networks: A Tutorial, Computer, v. 29 n. 3, p. 31-44, March 1996
  15. Simon Haykin, Neural Networks: A comprehensive foundation, 2nd Edition, Prentice Hall, 1998
  16. Alexander J. Faaborg, Using Neural Networks to Create an Adaptive Character Recognition System, March 2002, available at: http://web. media. mit. edu/~faabor/research/ cornell/hci_neuralnet work_finalPaper. pdf
  17. E. W. Brown, Character Recognition by Feature Point Extraction, unpublished paper authored at Northeastern University, 1992, available at: http://www. ccs. neu. edu/home/feneric/charrecnn. html
  18. Dori, Dov, and Alfred Bruckstein, ed. Shape, Structure and Pattern Recognition. New Jersey: World Scientific Publishing Co. , 1995.
  19. Gorsky, N. D. "Off-line Recognition of Bad Quality Handwritten Words Using Prototypes. " Fundamentals in Handwriting Recognition. Ed. Sebastiano Impedovo. New-York: Springer-Verlag, 1994.
  20. Impedovo, Sebastiano. "Frontiers in Handwriting Recognition. " Fundamentals in Handwriting Recognition. Ed. Sebastiano Impedovo. New-York: Springer-Verlag, 1994.
  21. Licolinet, Eric, and Olivier Baret. "Cursive Word Recognition: Methods and Strategies. " Fundamentals in Handwriting Recognition". Ed. Sebastiano Impedovo. New-York: Springer-Verlag, 1994.
  22. Simon, J. C. "On the Robustness of Recognition of Degraded Line Images. " Fundamentals in Handwriting Recognition". Ed. Sebastiano Impedovo. New-York: Springer-Verlag, 1994.
  23. Wang, Patrick Shen-Pei. "Learning, Representation, Understanding and Recognition of Words - An Intelligent Approach. " Fundamentals in Handwriting Recognition. Ed. Sebastiano Impedovo. New-York: Springer-Verlag, 1994.
  24. Yaeger, Larry S. , Brandyn J. Webb, and Richard F. Lyon. "Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton. " A. I. Magazine. 19(1): 73-89, 1998 Spring.
  25. Young, Tzay Y. , and King-Sun Fu, ed. Handbook of Pattern Recognition and Image Processing. New York: Academic Press, Inc. , 1996.
Index Terms

Computer Science
Information Sciences

Keywords

Gregg Shorthand Recognition Competitive Artificial Neural Network Shorthand Script Recognition