References
[1]. D. Acharya, N.V.S. Reddy, and K. Makkithaya (2008).
“Multilevel classifiers in Recognition of Handwritten Kannada Numerals”. In the Proceedings of world
Academy of Science, Engineering and Technology, pp.
308–313.
[2]. J.H. Alkhateed, J. Ren, J. Jiang, and H.A. Muhtaseb
(2011). “Offline Handwritten Arabic Cursive Text
Recognition using Hidden Markov Models and Reranking”.
Pattern Recognition Letters, Vol. 32,
pp.1081–1088.
[3]. V. N. M. Aradhya, G. H. Kumar, and S. Noushath
(2007). “Robust Unconstrained Handwritten Digit
Recognition using Radon Transform”. In the Proceedings
of International Conference on Signal Processing,
Communications and Networking (ICSCN’ 07), pp. 626 –
629.
[4]. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman
(1997). “Eigenfaces versus Fisherfaces: Recognition using
class Specific Linear Projection”. IEEE Transactions on
Pattern Analysis and Machine Intelligence, Vol. 19, pp.
711–729.
[5]. U. Bhattacharya and B.B.Chaudhari (2009).
“Handwritten Numeral databases of Indian Scripts and
Multistage Recognition of Mixed Numerals”. IEEE
Transaction on Pattern Analysis and machine
Intelligence, Vol. 31, pp. 444–457.
[6]. E.J. Candes and D.L. Donoho (1999). “Ridgelets: a
key to higher dimensional Intermittency?” Philosophical
Transaction of the Royal Society, pp. 2495–2509.
[7]. M.N. Do and M. Vetterli (2002). Finite Ridgelet
Transform for Image Representation”. In IEEE Transactions
on Image Processing.
[8]. L. Granai, F. Moschetti, and P. Vandergheynst (2003).
“Ridgelet Tansform applied to Motion Comprensated
Images”. In the Proceedings of ICASSP2003, pp.
381–384.
[9]. M. Li, C. Wang, and R. Dai (2008). “Unconstrained
handwritten character recognition based on WEDF and
Neural Network”. In IEEE Transaction on Image Processing
Theory, pp. 1143–1148.
[10]. S. A. Mahmoud and M. H. Abu-Amara (2010). “The
use of Radon Transform in handwritten Arabic (Indian)
Numerals Recognition”. Journal of World Scientific and Engineering Academy and Society (WSEAS), Vol. 9(3), pp.
252–267.
[11]. D. Nasaien, H. Haron, and S.S. Yahunaiz (2010).
“Support Vector Machine (SVM) for english handwritten
nd character recognition”. In the Proceedings of 2
International conference on Computer Engineering and
Applications, pp. 249–252.
[12]. C. Naveena and V. N. Manjunath Aradhya (2011).
“An impact of Ridgelet Transform in handwritten
recognition: A study on very large dataset of kannada
script”. In the Proceedings of International Conference
on World Congress on Information and Communication
Technologies (WICT-2011), pp. 622 – 625.
[13]. H. Nemmour and Y. Chibani (2011). “Handwritten
Arabic Word Recognition based on Ridgelet Transform
and Support Vector Machines”. In the Proceedings of
International Conference on High Performance
Computing and Simulation (HPCS), IEEE Computer
Society, pp. 357 – 361.
[14]. S.K. Niranjan, V. Kumar, G. Hemantha Kumar, and
V.N.M Aradhya (2009). “FLD based Unconstrained
Handwritten Kannada Character Recognition”.
International Journal of Database Theroy and
Application, Vol. 2, pp. 21–26.
[15]. S.K. Sangame, R.J. Ramteke, and R. Benne (2009).
“Recognition of Isolated Handwritten Kannada Vowels”. In
the Proceedings of Advances in Computational
Research, pp. 52–55.
[16]. N. Shanthi and K. Duriswamy (2009). “A Novel SVMbased
Handwritten Tamil Character Recognition System”.
Pattern Analysis Application, (Springer), Vol. 13, pp.
173–180.
[17]. T.H. Su, T.W. Zhang, D.J. Guan, and H.J. Huang
(2009). “Offline Recognition of Realistic Chinese
handwriting using Segmentation-Free Stratergy”. Pattern
Recognition, Vol. 42, pp. 167–182.
[18]. M. Turk and A. Pentland (1991). “Eigenfaces for
Recognition”. Journal of Cognition and Neuroscience,
Vol. 3, pp. 71–86.
[19]. H. Zhang, J. Yang, W. Deng, and J. Guo (2008).
“Handwritten Chinese Character Recognition using Local Discriminant Projection with prior information”. In the
th Proceedings of 19 International Conference on Pattern
Recognition (ICPR 2008), pp. 1–4.
[20]. C. Zhong, Y. Ding, and J. Fu (2010). “Handwritten Character Recognition based on 13-point feature of
Skeleton and Self-organizing Competition Network”. In the
Proceedings of International conference on Intelligent
Computation Technology and Automation, pp. 414–417.