An Effect of Ridgelet Transform on Various Distance Measure Techniques in Handwritten Character Recognition

Y.C. Kiran*, V.N. Manjunath Aradhya**, C. Naveena***
* Associate Professor, Department of Information Science and Engineering, Dayanada Sagar College of Engineering, Bengaluru, India
** Associate Professor, Department of MCA, S. J. College of Engineering, Mysuru, India.
*** Professor, Department of Computer Science and Engineering, HKBK College of Engineering, Bengaluru, India.
DOI : https://doi.org/

Abstract

The Ridgelet Transform [6] was introduced as a sparse expansion for functions of continuous spaces that are smooth away from discontinuities along lines. The powerful properties of the ridgelets are catching and representing monodimensional singularities in bi-dimensional space [8]. Using these effective properties, in this paper the authors propose an effect of Ridgelet Transform on various Similarity/Distance Measure Techniques namely Euclidean Distance, Modified Squared Euclidean Distance, Correlation Distance and Angle Distance for an unconstrained bi-lingual handwritten character recognition. Ridgelet Transform is used to extract a character image of low pass energy and is then fed to PCA for feature extraction. We conducted experiment on very large database of bi-lingual handwritten characters (Kannada and English). The database contains the samples of 22,600 and the effect of the proposed method is compared with the standard PCA & FLD methods. Among the above mentioned similarity/distance measure techniques the better recognition accuracy were achieved using angle distance measure.

Keywords

Hand Written Characters, Ridgelets, Distance Measure, Principal Component Analysis.

How to Cite this Article?

Kiran, Y. C., Aradhya, V. N. M., and Naveena, C. (2015). An Effect of Ridgelet Transform on Various Distance Measure Techniques in Handwritten Character Recognition. i-manager’s Journal on Pattern Recognition, 1(4), 11-20.

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.
If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.

Purchase Instant Access

Single Article

USD EUR INR
Print 35 35 200
Online 35 35 200
Print & Online 35 35 400