An Intelligent System Approach for Handwritten Kannada Word Recognition

M.S. Patel*, S.C. Linga Reddy**
* Associate Professor, Department of ISE, Dayananda Sagar College of Engineering, Bengaluru, India.
** Professor & HOD, Department of Computer Science and Engineering, Alpha College of Engineering, Bengaluru, India.
Periodicity:March - May'2015
DOI : https://doi.org/10.26634/jpr.2.1.3371

Abstract

The challenges in OCR system are many in the area of Handwritten Character and Word Recognition. From its very nature, Handwritten Character Word is a mixture of cursive and non-cursive segments. This leads to the problem of Recognition being significantly difficult. In this paper, the authors propose an intelligent system for recognition of handwritten Kannada words for recognition of the names of Districts and Taluks written on boards, land marks, etc. The proposed system applies an idea of subspace approach with popular Neural Network Architectures such as Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) for classification. This method is experimented on handwritten words comprising 189 (District & Taluk names of Karnataka State) classes.

Keywords

Handwritten Kannada Word Recognition, Subspace Approach, Generalized Regression Neural Network, Probabilistic Neural Network, Classification

How to Cite this Article?

Patel, M. S., and Reddy, S. L. (2015). An Intelligent Approach for Handwritten Kannada Word Recognition. i-manager’s Journal on Pattern Recognition, 2(1),10-15. https://doi.org/10.26634/jpr.2.1.3371

References

[1]. Adria Gimenez, Jesus Andres-Ferrer, Alfons Juan, (2014). “Discriminative Bernoulli HMMs for Isolated Handwritten Word Recognition”. Journal Pattern Recognition Letters, Vol.35, pp.157–168.
[2]. A. El-Yacoubi, M. Gilloux, (1999). “An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 8, pp.752-760.
[3]. Brijmohan Singh, Ankush Mittal, M.A. Ansari, Debashis Ghosh, (2011). “Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach”, International Journal on Computer Science and Engineering, Vol. 3, No. 4, pp.1658-1665.
[4]. Ankush Acharyya, Sandip Rakshit, Ram Sarkar, Subhadip Basu, Mita Nasipuri, (2013). “Handwritten Word Recognition using MLP Based Classifier: A Holistic Approach”, IJCSI International Journal of Computer Science Issues, Vol.10, No.2, pp. 422-427.
[5]. Mahdi Hamdani, Patrick Doetsch, Hermann Ney, (2014). “Improvement of Context Dependent Modeling for Arabic Handwriting Recognition”, International Conference on Frontiers in Handwriting Recognition, pp. 494-499.
[6]. Saeed Mozaffari, Karim Faez, Volker Margner, Haikal El-Abed, (2008). “Lexicon Reduction using Dots for Off-line Farsi/Arabic Handwritten Word Recognition”, Journal Pattern Recognition Letters, Vol. 29, No. 6, pp. 724–734.
[7]. M. Blumenstein, C. K. Cheng and X. Y. Liu, (2002). “New preprocessing Techniques for Handwritten Word Recognition”, Proceedings of the Second IASTED.
[8]. Yaregal Assabie, Josef Bigun, (2011). “Offline Handwritten Amharic Word Recognition”, Pattern Recognition Letters, Vol.32, pp.1089–1099.
[9]. Ahlam Maqqor, Akram Halli, and Khaled Satori, (2013). “A Multi-Stream HMM Approach to Offline Handwritten Arabic Word Recognition”, International Journal on Natural Language Computing (IJNLC), Vol. 2, No.4, pp.21-33.
[10]. N. Azizi, N. Farah, M. Sellami, (2005). “Off-line Handwritten Word Recognition using Ensemble of Classifier Selection and Features Fusion”, Vol.14, No.2, pp.141-150.
[11]. Dipak V. Koshti, Sharvari Govilkar, (2012). “Segmentation of Touching Characters in Handwritten Devanagari Script”, UACEE International Journal of Computer Science and its Applications, Vol.2, No.2, pp. 83-87.
[12]. Rajiv Kumar and Amardeep Singh, (2011). “Character Segmentation in Gurumukhi Handwritten Text using Hybrid Approach”, International Journal of Computer Theory and Engineering, Vol. 3, No. 4, pp. 499- 501.
[13]. Vikas J Dongre , Vijay H Mankar, (2011). “Devnagari Document Segmentation using Histogram Approach”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.3, pp. 46- 53.
[14]. Shuchi Kapoor and Vivek Verma, (2014). “Fragmentation of Handwritten Touching Characters in Devanagari Script”, International Journal of Information Technology, Modeling and Computing (IJITMC), Vol. 2, No. 1, pp. 11-21.
[15]. G.Vamvakas, B.Gatos, N. Stamatopoulos, and S.J.Perantonis, (2008). “A Complete Optical Character Recognition Methodology for Historical Documents”, IAPR Workshop on Document Analysis Systems, pp. 525 – 532.
[16]. Turk., M and A. Pentland, (1991). “Eigenfaces for Recognition”, Journal of Cognition and Neuroscience, Vol.3, No.1, pp. 71–86.
[17]. Wasserman P D, (1993). Advanced Methods in Neural Computing. pp. 155–161, Van Nostrand Reinhold, New York, USA.
[18]. T. Poggio and F. Girosi, (1990). “Networks for Approximation and Learning”, Proceedings of the IEEE, Vol.78, No.9, pp 1481–1497.
[19]. Donald F Specht, (1990). “Probabilistic Neural Networks”, Journal of Neural Networks, Vol.3, No.1, pp. 109–118.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

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.