A Connection Reduced Network for Similar Handwritten Chinese Character Discrimination

Yunxue Shao, Guanglai Gao, Chunheng Wang
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引用次数: 4

Abstract

One difficulty in handwritten Chinese character recognition (HCCR) is due to the large number of similar characters. In this study, we propose a connection reduced network (CRN) to discriminate similar pairs. Each hidden neuron in CRN is restricted to has one input signal and the strength of this input is set as a variable which is selected from the input of the network. Experimental results based on 100 similar pairs demonstrate that the proposed method yields highly competitive test recognition results compared to the state-of-the-art methods, while consuming less memory and time resources.
相似手写体汉字识别的连接减少网络
手写体汉字识别的一个难点是大量的相似字符。在这项研究中,我们提出了一个连接减少网络(CRN)来区分相似对。在CRN中,每个隐藏神经元被限制只有一个输入信号,并将该输入信号的强度设置为一个变量,该变量从网络的输入中选择。基于100对相似对的实验结果表明,该方法在消耗较少的内存和时间资源的情况下,取得了与现有方法相比极具竞争力的测试识别结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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