基于Tesseract_OCR的化工包装袋喷码质量检测算法

doi:10.16180/j.cnki.issn1007-7820.2022.07.005

Abstract

Abstract:

For the problems of low efficiency and high leakage rate in manual quality inspection of information printing on chemical packaging bags, a machine vision-based quality inspection method for printing codes on chemical packaging bags is designed in this study. Mean filtering and Gaussian bilateral filtering algorithms are used to pre-process the captured image, and then the character area is localized through a variable threshold algorithm based on local statistics. To solve the problem that the distance between the dots of the printout characters may be larger than the gap size between the characters, which leads to the formation of a connected domain with multiple characters sticking together after the binary image closure operation, the study proposes a dynamic character segmentation algorithm to improve the connected domain. The segmented character images are trained and recognized by Tesseract_OCR engine for classification. The experimental results show that the algorithm has the accuracy rate of 95.62% for coding quality detection, which can meet the requirements of chemical packaging bag coding quality inspection.

Key words: machine vision, Tesseract_OCR, chemical packaging bags, coding quality inspection, preprocessing, character localization, improved concatenated domain, character segmentation

CLC Number:

TP391.41

ZHANG Maolin,YE Qingzhou,PAN Xin,LU Hua. Quality Inspection Algorithm of Chemical Packaging Bag Coding Based on Tesseract_OCR[J].Electronic Science and Technology, 2022, 35(7): 27-31.

Figures/Tables 7

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Table 1.

References 17

[1]	Ouadid Y, Fakir M, Minaoui B. Tifinagh printed character recognition through structural feature extraction[J]. International Journal of Computer Vision and Image Processing, 2016, 6(2):42-53. doi: 10.4018/IJCVIP.2016070103
[2]	Chen F, Li B, Dong R, et al. High-performance OCR on packing boxes in industry based on deep learning[C]. Nanjing: Proceedings of the Pacific Rim International Conference on Artificial Intelligence, 2018.
[3]	Wei H, Wen Y, Wang W, et al. Convolutional neural network for machine-printed traditional mongolian font recognition[C]. Bangkok: Proceedings of the International Conference on Neural Information Processing, 2018.
[4]	周阳, 沈婷婷, 席志远, 等. 基于Halcon的日化瓶瓶底喷码质量检测算法研究[J]. 仪表技术与传感器, 2020(11):101-104.
	Zhou Yang, Shen Tingting, Xi Zhiyuan. Research on quality Detection algorithms based on Halcon for bottom spray code of daily chemical bottles[J]. Instrument Technique and Sensor, 2020(11):101-104.
[5]	Berg S A, So R H Y, Seo S Y. Application of optical character recognition with Tesseract in logistics management[J]. International Journal of Internet Manufacturing and Services, 2019, 6(3):285-304. doi: 10.1504/IJIMS.2019.10022461
[6]	王红艳, 朱利民, 张潘杰, 等. 一种利用外轮廓配准的活塞侧面缺陷检测方法[J]. 西安电子科技大学学报, 2019, 46(5):75-83.
	Wang Hongyan, Zhu Limin, Zhang Panjie, et al. Method for the detection of the piston side defect based on external contour registration[J]. Journal of Xidian University, 2019, 46(5):75-83.
[7]	杨云辉. 基于单目视觉的工件定位技术研究[J]. 电子科技, 2019, 32(12):72-75.
	Yang Yunhui. Research on workpiece location technology based on monocular vision[J]. Electronic Science and Technology, 2019, 32(12):72-75.
[8]	王震, 景军锋, 张缓缓, 等. 基于主结构提取与签名算法的织物缺陷检测[J]. 电子测量与仪器学报, 2019, 33(4):36-41.
	Wang Zhen, Jing Junfeng, Zhang Huanhuan, et al. Fabric defect detection based on main structure extraction and signature algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(4):36-41.
[9]	马旭东, 袁锐波. 基于图像检测的硬币面值识别方法[J]. 电子科技, 2020, 33(2):43-47.
	Ma Xudong, Yuan Ruibo. An identification method of coins denomination based on image detection[J]. Electronic Science and Technology, 2020, 33(2):43-47.
[10]	雷涛, 张肖, 加小红, 等. 基于模糊聚类的图像分割研究进展[J]. 电子学报, 2019, 47(8):1776-1791.
	Lei Tao, Zhang Xiao, Jia Xiaohong, et al. Research progress on image segmentation based on fuzzy clustering[J]. Acta Electronica Sinica, 2019, 47(8):1776-1791.
[11]	刘小燕, 吴鑫, 孙炜, 等. 基于形态学重建和GMM的球团颗粒图像分割[J]. 仪器仪表学报, 2019, 40(3):230-238.
	Liu Xiaoyan, Wu Xin, Sun Wei, et al. Image segmentation of pellet particles based on morphological reconstruction and GMM[J]. Chinese Journal of Scientific Instrument, 2019, 40(3):230-238.
[12]	Goh T Y, Basah S N, Yazid H, et al. Performance analysis of image thresholding: Otsu technique[J]. Measurement, 2018, 114(7):298-307. doi: 10.1016/j.measurement.2017.09.052
[13]	朱亚萍, 邱锦山, 杨成忠. 基于改进连通域算法的车牌字符分割方法[J]. 杭州电子科技大学学报(自然科学版), 2016, 36(2):48-51.
	Zhu Yaping, Qiu Jinshan, Yang Chengzhong. License plate character segmentation method based on improved connected domain algorithm[J]. Journal of Hangzhou Dianzi University(Natural Sciences), 2016, 36(2):48-51.
[14]	张红霞, 王灿, 刘鑫, 等. 复杂背景图像的字符识别算法研究[J]. 计算机测量与控制, 2019, 27(8):162-166.
	Zhang Hongxia, Wang Can, Liu Xin, et al. Automatic recognition algorithms research for printed characters with local exposure[J]. Computer Measurement & Control, 2019, 27(8):162-166.
[15]	Sporici D, Cusnir E, Boiangiu C A. Improving the accuracy of tesseract 4.0 OCR engine using convolution-based preprocessing[J]. Symmetry, 2020, 12(5):172-179. doi: 10.3390/sym12010172
[16]	Robby G A, Tandra A, Susanto I, et al. Implementation of optical character recognition using tesseract with the javanese script target in android application[J]. Procedia Computer Science, 2019, 157(4):499-505. doi: 10.1016/j.procs.2019.09.006
[17]	冯玉田, 侯玖廷, 顾乐易. 基于Tesseract-OCR的燃气表自动识别研究[J]. 电子测量技术, 2019, 42(21):82-86.
	Feng Yutian, Hou Jiuting, Gu Leyi. Reseatch on automatic identification of gas meter based on Tesseract-OCR[J]. Electronic Measurement Technology, 2019, 42(21):82-86.

本文检测算法结果目测校验结果	喷码质量合格/包	喷码质量不合格/包	目测校验结果合计
喷码质量合格/包	939	44	983
喷码质量不合格/包	0	17	17
本文检测算法结果合计/包	939	61	1 000

Quality Inspection Algorithm of Chemical Packaging Bag Coding Based on Tesseract_OCR

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 17

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZONG Shengkang,CHENG Jianpeng,ZHANG Xiliang. Automatic Detection Method of Crane Track Altitude Difference Based on Spot Position [J]. Electronic Science and Technology, 2022, 35(1): 21-28.
[2]	SI Mingming,CHEN Wei,HU Chunyan,YIN Zhong. Fundus Blood Vessel Image Segmentation Combining Resnet50 and U-Net [J]. Electronic Science and Technology, 2021, 34(8): 19-24.
[3]	MA Ding,WU Dingxiang,TANG Lijun. Research on the Defect Method of Detection Circular Ceramic Metal Coating [J]. Electronic Science and Technology, 2021, 34(6): 28-33.
[4]	YANG Lingxiao,WANG Zhenying,LIU Qunpo,WANG Gaowei,YANG Yanchao. Study of Apple Coloration Detection Algorithm Based on H-S Histogram [J]. Electronic Science and Technology, 2021, 34(5): 13-17.
[5]	GE Jing,LIU Zilong. The Algorithm Based on CNN and LSTM for Sleep Apnea Syndrome Detection [J]. Electronic Science and Technology, 2021, 34(2): 21-26.
[6]	YAN Zijie,WANG Jingmei,CHEN Zhuo,LIU Yu. Implementation of Software Programmable FPGA Network Measurement Engine Technology [J]. Electronic Science and Technology, 2021, 34(2): 27-32.
[7]	ZHU Jian'an,WEI Yunbing,ZHU Pengjie,JIANG Chengcheng,ZHU Chengming. Medium and Long-Term Load Forecasting Based on Optimized Grey Fourier Residual Correction [J]. Electronic Science and Technology, 2021, 34(12): 49-55.
[8]	SU Ye,LI Jun,YANG Haima,LIU Jin,JIANG Shenghua. Research on Rail Surface Defect Recognition Based on Faster R-CNN [J]. Electronic Science and Technology, 2020, 33(9): 63-68.
[9]	XIE Zhixuan,YAO Hongbing,FAN Ning,CHEN Feng. Connected Domain Label Detection Algorithm for Multi-target Lens [J]. Electronic Science and Technology, 2020, 33(4): 50-54.
[10]	ZHOU Wenkai,HAN Fang,KONG Weijian. Point-selective Geetest CAPTCHA Recognition Based on Faster-RCNN [J]. Electronic Science and Technology, 2019, 32(9): 42-46.
[11]	SUN Tengfei,JIANG Honghai. Research on Speed Measurement of Nozzle Material Liquid Droplet Based on Pattern Classification [J]. Electronic Science and Technology, 2019, 32(8): 33-37.
[12]	YANG Zhenchao，ZHANG Jiajia，ZHOU Lü，WU Fei. Potato Sorting Technology Research Using LabVIEW [J]. , 2017, 30(9): 72-.
[13]	ZHANG Jiajia，YANG Zhenchao，ZHOU Lü. The Cores Sorting System Research Using Image Processing Based on Matlab [J]. , 2017, 30(9): 50-.
[14]	CAO Weidong, ZHANG Zhongwei . Use of Machine Vision in Counting of the People in Building Lift [J]. , 2016, 29(9): 22-.
[15]	NIAN Lei, MAI Yunfei. A Profile Tolerance Measurement System Based on Machine Vision [J]. , 2016, 29(10): 104-.