摘要
目的为加强银行智能办理业务的设备性能,提高票据数字的识别效率,研究一种改进的算法来获得更高的数字识别效果。方法根据银行票据的印刷数字特性进行字符的提取和分割,经过图像采集、降噪、二值化之后使用起点直方图法结合步长法进行字符的分割,然后使用改进的LENET卷积神经网络用于提取数字特征,进行分类。结果通过实验,结果表明文中提出的方法进行复杂环境下的印刷数字识别,准确率达到95%以上,识别速率为1.169s/张。结论利用新的字符分割算法与改进的LENET神经网络相结合,可以很好地识别干扰强的印刷票据,准确率高。
The work aims to study an improved algorithm to obtain higher digital recognition effect,improve the recognition efficiency of the bill digital and strengthen the equipment performance of the bank’s intelligent handling business.Characters were extracted and divided according to the printed digital characteristics of bank notes.After image acquisition,noise reduction and binarization,the starting point histogram method was combined with the step size method for character segmentation,and then the improved LENET convolutional neural network was used to extract and classify digital features.Through experiments,the results showed that the proposed method can perform digital recognition in complex environments with an accuracy of more than 95%,and the recognition rate was 1.169 s/sheet.The new character segmentation algorithm combined with the improved LENET neural network can identify highly sensitive printed tickets with high accuracy.
作者
晏文仲
李光
YAN Wen-zhong;LI Guang(Tianjin University of Science and Technology,Tianjin 300222,China)
出处
《包装工程》
CAS
北大核心
2020年第21期244-250,共7页
Packaging Engineering
基金
天津市自然科学基金(17JCTPJC54900)。
关键词
票据识别
深度学习
卷积神经网络
字符识别
文本定位
ticket identification
deep learning
convolutional neural network
character recognition
text localization
