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Character-Level Convolutional Networks for Arithmetic Operator Character Recognition

机译:字符级卷积网络用于算术运算符字符识别

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Computer-assisted instruction systems have been broadly applied to help students solve math problems, One of the challenges of intelligent instruction systems is how to make the machine automatically understand and recognize arithmetic operator characters. In this paper, we study the problem of automatic recognition of arithmetic operator characters based on character-level convolutional networks. Our work consists of three parts. The first part is to generate training data and test data for our convolutional neural network. The second is to split the input data into character-level and convert each character to a vector by using word embedding technology. The last step is to train our convolutional network and analyze the accuracy of recognitions of our proposed convolutional network. The experimental results show that our proposed network can correctly classify the arithmetic operator characters.
机译:计算机辅助教学系统已广泛应用于帮助学生解决数学问题。智能教学系统的挑战之一是如何使机器自动理解和识别算术运算符。本文研究了基于字符级卷积网络的算术运算符自动识别问题。我们的工作包括三个部分。第一部分是为我们的卷积神经网络生成训练数据和测试数据。第二个方法是使用单词嵌入技术将输入数据拆分为字符级别,并将每个字符转换为矢量。最后一步是训练我们的卷积网络,并分析我们提出的卷积网络识别的准确性。实验结果表明,我们提出的网络可以正确分类算术运算符。

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