Publication Type:

Conference Paper

Source:

9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007) (2007)

Keywords:

Character recognition, Data mining, Dictionaries, Handwriting recognition, Image recognition, Image segmentation, Learning, Neural networks, Neurons, Optical character recognition software

Abstract:

<p>This paper presents the recognition of handwritten Hindi Characters based on the modified exponential membership function fitted to the fuzzy sets derived from features consisting of normalized distances obtained using the Box approach. The exponential membership function is modified by two structural parameters that are estimated by optimizing an objective function that includes the entropy and error function. A Reuse Policy that provides guidance from the past policies is utilized to improve the reinforcement learning. This relies on the past errors exploiting the past policies. The Reuse Policy improves the speed of convergence of the learning process over the strategies that learn without reuse and combined with the use of the reinforcement learning, there is a 25-fold improvement in training. Experimentation is carried out on a database of 4750 samples. The overall recognition rate is found to be 90.65%.</p>

Cite this Research Publication

M. Hanmandlu, Dr. Oruganti Venkata Ramana Murthy, and Madasu, V. K., “Fuzzy Model Based Recognition of Handwritten Hindi Characters”, in 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007), 2007.

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