[1]朱立倩.基于深度学习的数显仪表字符识别[J].计算机技术与发展,2020,30(06):141-144.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 027]
　ZHU Li-qian.Character Recognition of Digital Display Instrument Based on Deep Learning[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):141-144.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 027]
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基于深度学习的数显仪表字符识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年06期
页码:
141-144
栏目:
应用开发研究
出版日期:
2020-06-10

文章信息/Info

Title:
Character Recognition of Digital Display Instrument Based on Deep Learning
文章编号:
1673-629X(2020)06-0141-04
作者:
朱立倩
中国石油大学(华东) 计算机科学与技术学院,山东 青岛 266580
Author(s):
ZHU Li-qian
School of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China
关键词:
数显仪表; 卷积神经网络; 注意力机制; 字符检测; 字符识别
Keywords:
digital display instrument; convolutional neural network; attention mechanism; character detection; character recognition
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 06. 027
摘要:
在许多工业场景中,需要记录仪表的数据,将数据录入到电脑,这不仅耗时耗力,而且两次的转录可能导致错误的发生。 为了提高监控效率,需要对数显仪表数据进行自动识别。针对传统字符分割方法适应性差,准确度低的不足,提出了一种基于深度学习的自动识别数显仪表字符的方法,由字符区域定位网络及字符识别网络构成。 字符区域定位网络为改进的 Faster R-CNN,将 Faster R-CNN 的骨干网络改为 ResNeXt-101,感兴趣区域池化操作改为精确的感兴趣区域池化操作,以提高分类及定位的准确性。 字符识别网络由卷积神经网络和加入注意力机制的长短时记忆网络构成,注意力机制的加入提高了字符识别的准确性。 以变压器直流电阻测试仪为具体应用对象,实验结果显示,该方法可以达到 95% 的准确率。
Abstract:
In many industrial scenarios, it is necessary to record the data of the digital display instrument and then input it into the computer,which is not only time-consuming and laborious, but also two transcriptions may lead to errors. In order to improve the monitoring efficiency,it is necessary to recog-nize the data of the digital display instrument automatically. Aiming at the shortcomings of poor adaptability and low accuracy of traditional character segmentation methods,we propose a method of automatic recognition of digital display instrument characters based on deep learning,which is composed of character region location network and character recognition network. The character region location network is improved Faster R-CNN. The backbone network of Faster R-CNN is changed to ResNeXt-101,and the ROI-pooling is changed to precise ROI-pooling to improve the accuracy of classification and location. Character recognition network is composed of convolutional neural network and long short-term memory network with attention mechanism,which improves the accuracy of character recognition. Taking transformer DC resistance tester as a specific application object,the experimental results show that the proposed method can achieve 95% accuracy.

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更新日期/Last Update: 2020-06-10