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Recognizing pale letters and color letters with multi-layered neural networks

机译:利用多层神经网络识别浅色字母和彩色字母

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Neural networks (NNs) on backpropagation (BP) are capable of carrying out the required mapping because NNs can absorb vagueness of input patterns. In this paper, a system is proposed that can recognize pale letters and color letters without the preprocessor. The basic NNs used have three layers. However the method to increase the number of layers is proposed. We construct a multi(4,5)-layered NN with four and five layers on BP. As a result, increasing the number of layers makes the limit of recognizable density lower. The 4,5-layered NN can recognize almost white color letters written on white paper. It has the ability to recognize multi-valued data like uneven density letters and color letters.
机译:反向传播(BP)的神经网络(NN)能够执行所需的映射,因为NN可以吸收输入模式的模糊性。本文提出了一种无需预处理器即可识别浅色字母和彩色字母的系统。所使用的基本NN具有三层。但是,提出了增加层数的方法。我们在BP上构造了一个具有四层和五层的多层(4,5)NN。结果,增加层数使得可识别密度的极限降低。 4,5层的NN可以识别写在白纸上的几乎白色的字母。它具有识别多值数据的能力,例如不均匀密度字母和彩色字母。

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