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Hindi character recognition using RBF neural network and directional group feature extraction technique

机译:基于RBF神经网络和方向群特征提取技术的印地语字符识别

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摘要

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.
机译:在本文中,已经针对梯度特征的八个方向值实现了径向基函数(RBF)神经网络,用于手写北印度语字符识别。通过使用从不同年龄组的不同人收集的不同笔迹中的不同样本来训练字符识别系统。具有一输入一输出层的径向基函数网络已用于训练RBF网络。实验研究了RBF神经网络的识别精度,训练时间和分类时间。通过实施RBF网络获得的识别精度,训练时间和分类时间已与先前相关工作即反向传播神经网络获得的结果进行了比较。比较结果表明,具有方向性特征的RBF提供的识别精度略有降低,减少了训练和分类时间。

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