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Edge Based Graph Neural Network to Recognize Semigraph Representation of English Alphabets

机译:基于边缘的图神经网络识别英文字母的图形表示

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Graph Neural Network based on edges is introduced in this paper and is used to recognize the English uppercase alphabets treating their corresponding graphs as semigraphs. Graph Neural Network(GNN) is a connectionist model comprising of two feedforward neural networks (FNN) called transition network and output network connected by recurrent architecture according to the graph topology. The characteristics of the edges in a graph are considered as input for the transition network and the stabilized output of the transition network are taken as input for the output network. Edge based GNN is trained using error gradient method. Experimental results show that GNN is able to identify all the 26 graphs of alphabets correctly.
机译:本文介绍了基于边缘的图神经网络,用于识别英文大写字母,并将其对应的图视为半图。图神经网络(GNN)是一种连接器模型,由两个前馈神经网络(FNN)(称为过渡网络)和根据图拓扑通过递归体系结构连接的输出网络组成。图中边缘的特性被视为过渡网络的输入,过渡网络的稳定输出被视为输出网络的输入。使用误差梯度法训练基于边缘的GNN。实验结果表明,GNN能够正确识别所有26个字母图形。

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