Study on Instrument Ghosting Character Recognition Method for Predicting Binarization Threshold by BP Network
[1]SUN Guodong,JIANG Yajie,XU Liang,et al.Study on Instrument Ghosting Character Recognition Method for Predicting Binarization Threshold by BP Network[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):28-33.[doi:10.13705/j.issn.1671-6833.2020.04.011]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41
Number of periods:
2020 04
Page number:
28-33
Column:
Public date:
2020-08-12
- Title:
- Study on Instrument Ghosting Character Recognition Method for Predicting Binarization Threshold by BP Network
- Keywords:
- uneven illumination; ghost image; character recognition; prediction threshold; LeNet-5; BP neural network中图分类号：TP391.41
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2020.04.011
- Abstract:
- During instrument digital image acquisition,there were many phenomena of uneven illumination and character double shadow,which led to the difficulty of binarization and low recognition rate.A new binarization method is proposed.Before image binarization,the image would be preprocessed because of the poor image quality.Due to uneven illumination,the color image correction method based on nonlinear function was used.In view of the imaging ghosting,the image gray scale distribution statistics were taken as the input,and the adaptive binarization global threshold is used as the label of prediction model to train BP neural network.The trained BP network was used to predict the global threshold and binarize the image,in order to achieve the separating the ghosting.At the same time,the improved LeNet-5 network was adopted to recognize the single character after segmentation.The experimental results showed that the proposed binarization method was better than the classical methods,and the improved LeNet-5 could satisfy the instrument character recognition after segmentation,with the recognition rate of 98.94%,and the single character recognition time of only 0.0014s.
- References:
-
[1] 孙国栋,梅术正,汤汉兵,等.基于密度特征与KNN算法的最优特征维数选择[J].现代电子技术,2018,41(16):80-83.[2] 李智成,李文婷,梅术正,等.基于机器视觉的高压计量数显表自动校对系统[J].电测与仪表,2017,54(20):105-109,121.[3] 李娜.利用灰度变换法增强数字图像[J].北京工业职业技术学院学报,2009,8(3):36-39.[4] KANDHWAY P,BHANDARI A K,SINGH A.A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization[J].Biomedical signal processing and control,2020,56:101677.[5] KIM J Y,KIM L S,HWANG S H.An advanced contrast enhancement using partially overlapped sub-block histogram equalization[J].IEEE transactions on circuits and systems for video technology,2001,11(4):475-484.[6] 梁琳,何卫平,雷蕾,等.光照不均图像增强方法综述[J].计算机应用研究,2010,27(5):1625-1628.[7] JIN H,TU L,DENG X.Night image enhancement algorithm based on retinex theory[J].International journal of advancements in computing technology,2011,3(10):291-298.[8] 董静薇,徐博,马晓峰,等.基于同态滤波及多尺度Retinex的低照度图像增强算法[J].科学技术与工程,2018,18(22):238-242.[9] 任崇巍.基于双边滤波和Retinex算法的货车图像预处理方法[J].现代城市轨道交通,2019(6):39-43.[10] 刘健,郭潇,徐鑫龙,等.基于Retinex理论的低照度图像增强技术[J].火力与指挥控制,2019,44(9):139-143.[11] WANG W C,CHEN Z X,YUAN X H,et al.Adaptive image enhancement method for correcting low-illumination images[J].Information sciences,2019,496:25-41.[12] 查刘根,谢春萍.应用四层BP神经网络的棉纱成纱质量预测[J].纺织学报,2019,40(1):52-56,61.[13] 夏克文,李昌彪,沈钧毅.前向神经网络隐含层节点数的一种优化算法[J].计算机科学,2005,32(10):143-145.[14] 冉成科,夏向阳,杨明圣,等.基于日类型及融合理论的BP网络光伏功率预测[J].中南大学学报(自然科学版),2018,49(9):2232-2239.[15] 范伟,林瑜阳,李钟慎.基于BP神经网络的压电陶瓷蠕变预测[J].计量学报,2017,38(4):429-434.[16] 李标,王磊,朱金营,等.BP网络训练函数选取及其岩土工程应用分析[J].西部探矿工程,2008,20(10):58-60.[17] 李少辉,周军,刘波,等.基于机器视觉和神经网络的低质量文本识别研究[J].机电工程,2018,35(9):1006-1010.
- Similar References:
Memo
-
Last Update:
2020-10-06