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Research on Method of Character Recognition Based on Hough Transform and RBF Neural Network

机译:基于霍夫变换和RBF神经网络的字符识别方法研究

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A method of character recognition based on Hough transform and RBF neural network is proposed through research on weight accumulation algorithm of Hough transform. According to the feature of characters’ structure by using the duality of point-line Hough transform was done. In this method, the number of the points on the same line in parameter space and the position coordinates of the elements in image mapping space were taken to RBF neural network recognition system as characteristic input vector. It reduced the dimension of character feature vector and reflected the overall distribution of character lattice and the essential feature of character shape. The simulation results indicated there were some merits in this improved method: capability of recognition is strong, the quantity of calculation is small, and the speed of calculation is quick.
机译:通过对霍夫变换的权重累积算法的研究,提出了一种基于霍夫变换和RBF神经网络的字符识别方法。根据字符结构的特点,利用点线霍夫变换的对偶性进行了处理。该方法将参数空间中同一行上的点数和图像映射空间中元素的位置坐标作为特征输入向量送入RBF神经网络识别系统。它减小了字符特征向量的维数,反映了字符格的整体分布和字符形状的本质特征。仿真结果表明,该改进方法具有识别能力强,计算量小,计算速度快的优点。

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