Neural Networks are being used for character recognition from last many years but most of the works were reported to English character recognition. Character recognition is one of the applications of pattern recognition, which has enormous scientific and practical interest. Many scientific efforts have been dedicated to pattern recognition problems and much attention has been paid to develop recognition system that must be able to recognize a character. The main driving force behind neural network research is the desire to create a machine that works similar to the manner our own brain works. Neural networks have been used in a variety of different areas to solve a wide range of problems. A very little work has been reported for Handwritten Hindi Character recognition. In this paper, we have implemented Gradient feature extraction technique, which provides more than 94% recognition accuracy. We have acquired 1000 samples of handwritten Hindi characters by initializing the mouse in graphics mode. The 500 samples have been used for training the network (Train Data) and remaining 500 samples have been used for testing the network (Test Data). The system has been trained using several different forms of handwritings provided by both male and female participants of different age groups. Finally, this rigorous training results an automatic HCR system using MLP network. The error backpropagation algorithm has been used to train the MLP network. A comparative analysis was performed by implementing both global input and Gradient feature input. We have concluded that gradient feature extraction technique provides better recognition accuracy with reduced training time.
In this paper, we have applied a new feature extraction technique to calculate only twelve directional feature inputs depending upon the gradients. Features extracted from handwritten characters are directions of pixels with respect to their neighboring pixels. These inputs are given to a back propagation neural network with one hidden layer and one output layer. An analysis has been also carried out to compare the recognition accuracy, training time and classification time of newly developed feature extraction technique with some of the existing techniques. Experimental result shows that the new approach provides better results as compared to other techniques in terms of recognition accuracy, training time and classification time. The work carried out in this paper is able to recognize all type of handwritten characters even special characters in any language.
In this paper, Radial Basis Function (RBF) neural Network has been implemented on eight directional values of gradient features for handwritten Hindi character recognition. The character recognition system was trained by using different samples in different handwritings collected of various people of different age groups. The Radial Basis Function network with one input and one output layer has been used for the training of RBF Network. Experiment has been performed to study the recognition accuracy, training time and classification time of RBF neural network. The recognition accuracy, training time and classification time achieved by implementing the RBF network have been compared with the result achieved in previous related work i.e. Back propagation Neural Network. Comparative result shows that the RBF with directional feature provides slightly less recognition accuracy, reduced training and classification time.
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