Bi-lingual Handwritten Character and Numeral Recognition using Multi-Dimensional Recurrent Neural Networks (MDRNN)

The key to the continued success of ANN depends, considerably, on the use of hybrid structures implemented on cooperative frame-works. Hybrid architectures provide the ability to the ANN to validate heterogeneous learning paradigms. This work describes the implementation of a set of Distributed and Hybrid ANN models for Character Recognition applied to Anglo-Assamese scripts. The objective is to describe the effectiveness of Hybrid ANN setups as innovative means of neural learning for an application like multilingual handwritten character and numeral recognition.

Authors:

• Kandarpa Kumar Sarma

Keywords:

• Assamese
• Feature
• Recurrent.

References:

[1] R. Rojas, Neural Networks A Systematic Introduction, Springer, Berlin,
1996.
[2] S. Haykin, Neural Networks- A Comprehensive Foundation, 2nd Ed.,
Pearson Education, New Delhi, 2003.
[3] J. L. McClelland and D. E. Rumelhart, "Distributed Memory and the Representation
of General and Specific Information," Journal of Experimental
Psychology: General, 114, pp. 159-188 1985.
[4] D. E. Rummelhart, G. E. Hinton and R. J. Williams, "Learning Representations
by Back-Propagation Errors," Nature, 323, pp. 533-536 1986.
[5] T. Alvager, D. P. Beach and D. Herrmann, "A Hardware Implementation
of Artificial Neural Networks," Advanced Neural Computing Research
Center Annual Report, Aspen Technology Indiana State University, Terre
Haute, IN, USA, 2001.
[6] M. Misra, "Implementation of Neural Networks on Paralel Architectures",
PhD Thesis Presented to the Faculty of the Graduate School University
of Southern California, December, 1992.
[7] U. A. Muler, A. Gunzinger and W. Guggenbuh, "Fast Neural Net
Simulation with a DSP Processor Array", Electronics Laboratory, Swis
Federal Institute of Technology, February, 1993.
[8] N. B. Serebdzija, "Simulating Artificial Neural Networks on Parallel
Architectures", Computer, Vol. 29, No. 3, pp. 56-63, 1996.
[9] T. Schoenauer, A. Jahnke, U. Roth and H. Klar, "Digital Neurohardware:
Principles and Perspectives", Neuronal Networks in Applications - NN-98
- Magdeburg, pp. 101-106, 1998
[10] N. Sundararajan and P. Saratchandran , "Parallel Architectures for
Artificial Neural Networks ", Journal of the Institute of Electrical and
Electronics Engineers Inc., 1998.
[11] T. Heirs, "A High Performance Multiprocessor DSP System", Masters
Thesis, Computer Science and Artificial Intelligence Laboratory, Massachusetts
Institute of Technology, May, 2001
[12] K. K. Sarma, P. K. Bora and C. Mahanta, "Innovative Feature Set for
Multi Layered Perceptron (MLP) Based Assamese Character Recognition",
Proceedings of 2nd Indian International Conference on Artificial
Intelligence (IICAI-2005), pp. 473- 491, 2005.
[13] K. K. Sarma, "Novel Feature Set for Neural Character Recognition " ,
Proc. of the 5th International Symposium on Robotics and Automation,
San Miguel Regla Hidalgo, Mexico , pp. 409-414, August, 2006.
[14] K. K. Sarma, "MLP-based Assamese Character and Numeral Recognition
using an Innovative Hybrid Feature Set", Proc. Of the Proceedings
of 3rd Indian International Conference on Artificial Intelligence (IICAI-
2007), Pune, India , pp. 585 -600, December, 2007.
[15] K. K. Sarma, "Modified Hybrid Feature Set for Assamese Character
and Anglo-Assamese Hand Written Numeral Recognition", International
Journal of Imaging, Volume 1, pp. 13 -28, Autumn 2008.
[16] K. Bhattacharjya and K. K. Sarma, "ANN-based Innovative Segmentation
Method for Handwritten text in Assamese", IJCSI International
Journal of Computer Science Issues, Vol. V, No. IJCSI-2009-10-20,
October, 2009.