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卷积神经网络的民国纸币序列号识别系统

Paper currency serial number recognition system research of the Republic of China based on convolutional neural network

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【作者】 沈成龙王笑梅王晨

【Author】 SHEN Chenglong;WANG Xiaomei;WANG Chen;College of Information,Electrical and Mechanical Engineering,Shanghai Normal University;

【通讯作者】 王笑梅;

【机构】 上海师范大学信息与机电工程学院

【摘要】 实现了深度学习的民国纸币序列号自动识别系统.提取、分割民国纸币序列号字符,对单个字符进行预处理,裁剪字符空白区域,归一化字符大小,并使用卷积神经网络进行识别.实验结果表明:在纸币存在污迹、褶皱的情况下,所提民国纸币序列号识别系统能够减少人工录入的工作量,单个字符的识别精度高于99.99%.

【Abstract】 An automatic recognition system of paper currency serial numbers of the Republic of China was realized by deep learning in this paper. Firstly,the characters of paper currency serial numbers of the Republic of China were extracted and segmented.Secondly,pre-processing for each character was conducted and the blank character zone was clipped in order to normalize the character size.Lastly,the characters were recognized by the convolutional neural network.The experimental results showed that the paper currency serial number recognition system proposed in the paper could reduce the workload of manual entry while there were stains and wrinkles existing on the paper currency.The recognition accuracy of a single character could reach more than 99.99%.

  • 【文献出处】 上海师范大学学报(自然科学版) ,Journal of Shanghai Normal University(Natural Sciences) , 编辑部邮箱 ,2020年04期
  • 【分类号】TP391.41;F822.9;TP183
  • 【下载频次】109
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