Rajan Transform Based Spectral Analysis of Handwritten Characters

Jammi Ashok; Kuntigorla Saidulu; Bayisa Taye Mulatu

doi:doi:10.11648/j.ajnc.20160503.12

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Rajan Transform Based Spectral Analysis of Handwritten Characters

Jammi Ashok, Kuntigorla Saidulu, Bayisa Taye Mulatu

Published in American Journal of Networks and Communications (Volume 5, Issue 3)

Received: 18 May 2016 Accepted: 12 June 2016 Published: 11 July 2016

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Abstract

Homomorphic transforms are better suited for pattern recognition or classification. In general, homomorphic maps are not invertible and hence they are known as transformations. So, they do not fall under the category of mathematical transforms. But if the inverse of a transformation is obtained using an algorithm or a semi-decision procedure the transformation could be called a transform in the loose sense. Nonlinear homomorphic operators are not meant for analysis and synthesis, but they are used for classification. In this context, efforts were made to search for a homomorphic map which could be examined for character recognition. One such nonlinear homomorphic map has been identified as Rajan Transform. This paper provides details of this transform and its working principle in recognition of handwritten characters.

Published in	American Journal of Networks and Communications (Volume 5, Issue 3)
DOI	10.11648/j.ajnc.20160503.12
Page(s)	60-67
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Rajan Transform, Hadamard Transform, Homomorphic Transform

References

[1]	Rajan, E. G., ‘Symbolic Computing, Signal and Image Processing’, Anshan Publications, 6 Newlands Road, Tunbridge Wells, Kent TN4 9AT, United Kngdom, 2003.
[2]	Rajbala Tokas & Aruna, B 2012, ‘A comparative analysis of feature extraction techniques for handwritten character recognition’, International Journal of Advanced Technology and Engineering Research, vol. 2, no. 4, pp. 215-219.
[3]	Amritha Sampath, Tripti, C & Govindaru V 2012, ‘Freeman code based off-line handwritten character recognition for Malayalam using Back propagation neural networks’, Advance computing: An international journal, vol. 3, no. 4, pp. 51-58.
[4]	Ashutosh, A, Rajneesh Rani & Renu Dhir 2012, ‘Handwritten Devanagari Character Recognition Using Gradient Features’, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 5, pp. 85-90.
[5]	Rajib Lochan Das, Binod Kumar Prasad & Goutam Sanyal 2012, ‘HMM based Offline Handwritten Writer Independent English Character Recognition using Global and Local Feature Extraction’, International Journal of Computer Applications (0975 - 8887), vol. 46, no. 10, pp. 45-50.
[6]	Ashok, Jammi & Rajan, EG 2011, ‘Off-Line Hand Written Character recognition using Radial Basis Function’, in International Journal of Advanced Networking and Applications (IJANA), vol. 2, no. 4, pp. 792-795.
[7]	Aiquan Yuan, Gang Bai, Lijing Jiao & Yajie Liu 2012, ‘Off-line Handwritten English Character Recognition based on Convolutional nneural network’, Document Analysis Systems (DAS), 2012 10th IAPR International Workshop, vol., no., pp. 125, 129.
[8]	Alam, FI & Banik, B ‘Off-line isolated bangla handwritten character recognition using spatial relationships,’ Informatics, Electronics & Vision (ICIEV), 2013 International Conference on, vol., no., pp. 1,6, 17-18 May 2013
[9]	Ekambaram Naidu, M & Rajan, E G 2009, ‘Rajan Transform and its uses in Pattern Recognition’, Informatica, vol. 33, pp. 213-220.
[10]	Ashok, Jammi & Rajan, EG 2012, ‘Robust Technique based Off-Line Hand Written Character recognition’, European Journal of Scientific Research, vol. 84, no. 1.
[11]	Rajan, E G, ‘On the Notion of Generalized Rapid Transformation’, World multi conference on Systemics, Cybernetics and Informatics, Caracas, Venezuela, July 7-11, 1997
[12]	Rajan, EG 1993, ‘Cellular Logic Array Processing Techniques for High- Throughput Image Processing Systems’, Invited paper, Sadhana, Special Issue on Computer Vision, The Indian Academy of Sciences, vol. 18, pp. 279-300.
[13]	Rajan, EG 1996, On the Notion of a Geometric Filter and its relevance in the neighborhood processing of digital images in hexagonal grids, Fourth International Conference on Control, Automation, Robotics and Vision, Westin Stamford, Singapore, organized by the Nanyand Technological University, Singapore.
[14]	Rajan, EG 2003, ‘Symbolic Computing – Signal and Image Processing’, B. S. Publications, Hyderabad.
[15]	Wanli Ouyang & Cham, WK ‘Fast algorithm for Walsh Hadamard Transform on sliding windows’, pattern analysis and machine intelligence, IEEE Transactions, vol. 32, no. 1.
[16]	Whah, S, Kumar, G & Govindaraju, V 2012, ‘Multilingual word spotting in offline handwritten documents,’ Pattern Recognition (ICPR), 2012 21st International Conference, pp. 310, 313.
[17]	Helger Lipmaa 2002, ‘On Differential Properties of Pseudo-Hadamard Transform and Related Mappings’, INDOCRYPT 2002, LNCS 2551.

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APA Style

Jammi Ashok, Kuntigorla Saidulu, Bayisa Taye Mulatu. (2016). Rajan Transform Based Spectral Analysis of Handwritten Characters. American Journal of Networks and Communications, 5(3), 60-67. https://doi.org/10.11648/j.ajnc.20160503.12

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ACS Style

Jammi Ashok; Kuntigorla Saidulu; Bayisa Taye Mulatu. Rajan Transform Based Spectral Analysis of Handwritten Characters. Am. J. Netw. Commun. 2016, 5(3), 60-67. doi: 10.11648/j.ajnc.20160503.12

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AMA Style

Jammi Ashok, Kuntigorla Saidulu, Bayisa Taye Mulatu. Rajan Transform Based Spectral Analysis of Handwritten Characters. Am J Netw Commun. 2016;5(3):60-67. doi: 10.11648/j.ajnc.20160503.12

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@article{10.11648/j.ajnc.20160503.12,
  author = {Jammi Ashok and Kuntigorla Saidulu and Bayisa Taye Mulatu},
  title = {Rajan Transform Based Spectral Analysis of Handwritten Characters},
  journal = {American Journal of Networks and Communications},
  volume = {5},
  number = {3},
  pages = {60-67},
  doi = {10.11648/j.ajnc.20160503.12},
  url = {https://doi.org/10.11648/j.ajnc.20160503.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160503.12},
  abstract = {Homomorphic transforms are better suited for pattern recognition or classification. In general, homomorphic maps are not invertible and hence they are known as transformations. So, they do not fall under the category of mathematical transforms. But if the inverse of a transformation is obtained using an algorithm or a semi-decision procedure the transformation could be called a transform in the loose sense. Nonlinear homomorphic operators are not meant for analysis and synthesis, but they are used for classification. In this context, efforts were made to search for a homomorphic map which could be examined for character recognition. One such nonlinear homomorphic map has been identified as Rajan Transform. This paper provides details of this transform and its working principle in recognition of handwritten characters.},
 year = {2016}
}

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TY  - JOUR
T1  - Rajan Transform Based Spectral Analysis of Handwritten Characters
AU  - Jammi Ashok
AU  - Kuntigorla Saidulu
AU  - Bayisa Taye Mulatu
Y1  - 2016/07/11
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnc.20160503.12
DO  - 10.11648/j.ajnc.20160503.12
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 60
EP  - 67
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20160503.12
AB  - Homomorphic transforms are better suited for pattern recognition or classification. In general, homomorphic maps are not invertible and hence they are known as transformations. So, they do not fall under the category of mathematical transforms. But if the inverse of a transformation is obtained using an algorithm or a semi-decision procedure the transformation could be called a transform in the loose sense. Nonlinear homomorphic operators are not meant for analysis and synthesis, but they are used for classification. In this context, efforts were made to search for a homomorphic map which could be examined for character recognition. One such nonlinear homomorphic map has been identified as Rajan Transform. This paper provides details of this transform and its working principle in recognition of handwritten characters.
VL  - 5
IS  - 3
ER  -

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Author Information

Jammi Ashok

School of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia
Kuntigorla Saidulu

School of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia
Bayisa Taye Mulatu

School of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia

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APA Style

Jammi Ashok, Kuntigorla Saidulu, Bayisa Taye Mulatu. (2016). Rajan Transform Based Spectral Analysis of Handwritten Characters. American Journal of Networks and Communications, 5(3), 60-67. https://doi.org/10.11648/j.ajnc.20160503.12

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ACS Style

Jammi Ashok; Kuntigorla Saidulu; Bayisa Taye Mulatu. Rajan Transform Based Spectral Analysis of Handwritten Characters. Am. J. Netw. Commun. 2016, 5(3), 60-67. doi: 10.11648/j.ajnc.20160503.12

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AMA Style

Jammi Ashok, Kuntigorla Saidulu, Bayisa Taye Mulatu. Rajan Transform Based Spectral Analysis of Handwritten Characters. Am J Netw Commun. 2016;5(3):60-67. doi: 10.11648/j.ajnc.20160503.12

Copy | Download

@article{10.11648/j.ajnc.20160503.12,
  author = {Jammi Ashok and Kuntigorla Saidulu and Bayisa Taye Mulatu},
  title = {Rajan Transform Based Spectral Analysis of Handwritten Characters},
  journal = {American Journal of Networks and Communications},
  volume = {5},
  number = {3},
  pages = {60-67},
  doi = {10.11648/j.ajnc.20160503.12},
  url = {https://doi.org/10.11648/j.ajnc.20160503.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160503.12},
  abstract = {Homomorphic transforms are better suited for pattern recognition or classification. In general, homomorphic maps are not invertible and hence they are known as transformations. So, they do not fall under the category of mathematical transforms. But if the inverse of a transformation is obtained using an algorithm or a semi-decision procedure the transformation could be called a transform in the loose sense. Nonlinear homomorphic operators are not meant for analysis and synthesis, but they are used for classification. In this context, efforts were made to search for a homomorphic map which could be examined for character recognition. One such nonlinear homomorphic map has been identified as Rajan Transform. This paper provides details of this transform and its working principle in recognition of handwritten characters.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Rajan Transform Based Spectral Analysis of Handwritten Characters
AU  - Jammi Ashok
AU  - Kuntigorla Saidulu
AU  - Bayisa Taye Mulatu
Y1  - 2016/07/11
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnc.20160503.12
DO  - 10.11648/j.ajnc.20160503.12
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 60
EP  - 67
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20160503.12
AB  - Homomorphic transforms are better suited for pattern recognition or classification. In general, homomorphic maps are not invertible and hence they are known as transformations. So, they do not fall under the category of mathematical transforms. But if the inverse of a transformation is obtained using an algorithm or a semi-decision procedure the transformation could be called a transform in the loose sense. Nonlinear homomorphic operators are not meant for analysis and synthesis, but they are used for classification. In this context, efforts were made to search for a homomorphic map which could be examined for character recognition. One such nonlinear homomorphic map has been identified as Rajan Transform. This paper provides details of this transform and its working principle in recognition of handwritten characters.
VL  - 5
IS  - 3
ER  -

Copy | Download