CFP last date
20 December 2024
Reseach Article

A Neural Network based Method for Recognition of Handwritten English Pitman's Shorthand

by Janmaya Kumar Mishra, Khursheed Alam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 6
Year of Publication: 2014
Authors: Janmaya Kumar Mishra, Khursheed Alam
10.5120/17822-8775

Janmaya Kumar Mishra, Khursheed Alam . A Neural Network based Method for Recognition of Handwritten English Pitman's Shorthand. International Journal of Computer Applications. 102, 6 ( September 2014), 31-35. DOI=10.5120/17822-8775

@article{ 10.5120/17822-8775,
author = { Janmaya Kumar Mishra, Khursheed Alam },
title = { A Neural Network based Method for Recognition of Handwritten English Pitman's Shorthand },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 6 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number6/17822-8775/ },
doi = { 10.5120/17822-8775 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:26.640710+05:30
%A Janmaya Kumar Mishra
%A Khursheed Alam
%T A Neural Network based Method for Recognition of Handwritten English Pitman's Shorthand
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 6
%P 31-35
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Pitman Shorthand method of documenting is normally practiced by stenographers to take dictation at speaking speed invented by Sir Isaac Pitman in 1837. Special graphical symbols are used in this method of representing phonetic compositions of the dictated text for certain interval. This shorthand representation itself is a compressed and encrypted format of the English text. The Pitman Shorthand Language (PSL) has the practical advantage of high speed of recording, more than 120–200 words per minute, because of which it is universally acknowledged. This recording medium has its continued existence in spite of considerable developments in speech processing systems, which are not universally established yet. In this method of documenting, speech is directly converted into phonetic strokes, where each phonetic stroke is a composition of consonants and vowels. These consonants are called consonant primitives or simply primitives. In order to exploit the vast transcribing potential of PSL a new area of research on automation of PSL processing is conceived. In this work, the Neural Network Classifier for recognition of PSL at word level is presented. All the activities such as preprocessing of data (image cropping, resizing and implementations) have been carried out using MATLAB. As per this experiment, 94% recognition accuracy is achieved.

References
  1. Hemadri, V. B. , B. Anami and C. N. Ravi Kumar. "A Novel Secant Based Method for Recognition of Handwritten Pitman Shorthand Language Consonants and Vowels. " International Conference on Advanced Computing and Communications (ADCOM 2006), 2006.
  2. Nagabhushan, P. and S. Murali. "Recognition of Pitman shorthand text using tangent feature values at word level. " Sadhana, vol. 28, no. 6, pp: 1037-1046, 2003.
  3. Yang Ma, Graham Leedham, Colin Higgins and Swe Myo Htwe. "Segmentation and recognition of phonetics features in handwritten Pitman shorthand". Pattern Recognition, vol. 41, pp. 1280-1294, 2008.
  4. Leedham, C. G. and Andy C. Downton. "Automatic recognition and transcription of Pitman's handwritten shorthand - an approach to shortforms. " Pattern Recognition, vol. 20, no. 3 pp: 341-348, 1987.
  5. Htwe, S. M. , Colin Higgins, Graham Leedham and Ma Yang. "Post-processing of Handwritten Pitman's Shorthand Using Unigram and Heuristic Approaches. " In Document Analysis Systems VI, pp. 332-336. Springer Berlin Heidelberg, 2004.
  6. Leedham, C. G. and Andy C. Downton. "On-line recognition of Pitman's handwritten shorthand - an evaluation of potential. " International journal of man-machine studies, vol. 24, no. 4, pp: 375-393, 1986.
  7. Sahoo, A. K, G. S. Mishra, and K. K. Ravulakollu. Sign Language Recognition: State of the Art. ARPN Journal of Engineering and Applied Sciences. vol. 9, no. 2, pp: 116-134, 2014.
  8. Vamvakas, G. , B. Gatos and J. Stavros. Perantonis. "Handwritten character recognition through two-stage foreground sub-sampling". Pattern Recognition. vol. 43, no. 8, pp: 2807-2816, 2010.
  9. Singh, Y. P. , Khare, A. and Gupta, A. "Analysis of Hopfield Auto associative Memory in the Character Recognition", International Journal on Computer Science and Engineering, Vol. 2, No. 3, pp: 500-503, 2010.
  10. Rajasekaran, S. and G. A. V Pai. "Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications", Prentice Hall of India, ISBN-978-81-203-2186-1, 2007.
  11. Sahoo, A. K. and K. K. Ravulakollu. "Indian Sign Language Recognition Using Skin Color Detection". International Journal of Applied Engineering Research (IJAER). vol. 9, no. 20, pp: 7347-7360, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Pitman's Shorthand Neural Network Classifier Hierarchical Centroid