Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240100031-9.doi: 10.11896/jsjkx.240100031

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Lightweight and Efficient Recognition Method for Chinese Character Click-based CAPTCHA

JIN Xinhao, CHI Kaikai   

  1. School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310013,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:JIN Xinhao, born in 1998, postgraduate.His main research interest is robot process automation.
    CHI Kaikai,born in 1980,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.72583S).His main research interests include wireless networks and machine learning.
  • Supported by:
    General Program of the National Natural Science Foundation of China(62272414).

Abstract: With the advent of digitalization,enterprises increasingly rely on robotic process automation technologies to reduce costs and improve efficiency,thus maintaining competitiveness.However,the automation level is hindered by the challenge of Chinese character click-based CAPTCHA recognition in certain process steps.Existing research on this problem faces difficulties in dataset creation,poor model generalization performance,and an imbalance between model complexity and performance.To address these issues,this paper proposes a low-cost dataset creation approach and a lightweight Chinese character click-based CAPTCHA recognition method with excellent generalization performance.Specifically,a significantly lightweight version of the YOLOv8-n model,tailored for Chinese cha-racter detection,is employed in this study.Subsequently,preprocessing operations such as segmentation and rectification are applied to the CAPTCHA images.The highly versatile PaddleOCR model is utilized for Chinese character recognition,reducing the cost of scene adaptation.Furthermore,the best matching result is obtained through the recognition probability matrix,further enhancing accuracy.Additionally,a semi-automatic Chinese character detection dataset construction process is designed and made publicly available.This research aims to promote the development of automated Chinese character click-based CAPTCHA recognition techniques,enhance the level of enterprise process automation.

Key words: Process automation, Verification code recognition, YOLOv8, PaddleOCR, Lightweight

CLC Number: 

  • TP391
[1]ENRÍQUEZ J G,JIMÉNEZ-RAMÍREZ A,DOMÍNGUEZ-MAYO F J,et al.Robotic process automation:a scientific and industrial systematic mapping tudy[J].IEEE Access,2020,8:39113-39129.
[2]LIANG Y.Research on the Application of Financial RobotProcess Automation Based on Machine Learning[C]//2021 International Conference on Electronic Information Technology and Smart Agriculture(ICEITSA).IEEE,2021:474-477.
[3]HANDOKO B L,LINDAWATI A S L,MUSTAPHA M.Robotic process automation in audit 4.0[C]//The 2021 12th International Conference on E-business,Management and Econo-mics.2021:128-132.
[4]BRANDSTATTER C,TSCHANDL M,MITTERBACK C.AGeneric Process Model for the Introduction of Robotic Process Automation in Financial Accounting[C]//Proceedings of the 2023 9th International Conference on Computer Technology Applications.2023:12-18.
[5]AHMET UNAL M,BOLUKBAS O.The Acquirements of Digitalization with RPA(Robotic Process Automation) Technology in the Vakif Participation Bank[C]//Proceedings of the 4th International Conference on Information Science and Systems.2021:68-73.
[6]LIU S,QI X,LI H.Practice of Robot Process Automation in Power Grid Dispatching Report[C]//Proceedings of the 2022 4th International Conference on Robotics,Intelligent Control and Artificial Intelligence.2022:212-216.
[7]RATIA M,MYLLÄRNIEMI J,HELANDER N.Roboticprocess automation-creating value by digitalizing work in the private healthcare?[C]//Proceedings of the 22nd International Academic Mindtrek Conference.2018:222-227.
[8]SGANDERLA R B,FANTINATO M,THOM L H.RoboticProcess Automation in Latin American Organizations:Survey and Evaluation of the Current State of Technology Adoption[C]//Proceedings of the XIX Brazilian Symposium on Information Systems.2023:459-467.
[9]KEDZIORA D,HYRYNSALMI S.Turning Robotic ProcessAutomation onto Intelligent Automation with Machine Learning[C]//Proceedings of the 11th International Conference on Communities and Technologies.2023:1-5.
[10]KHOLIYA P S,KAPOOR A,RANA M,et al.Intelligentprocess automation:The future of digital transformation[C]//10th International Conference on System Modeling & Advancement in Research Trends(SMART 2021).IEEE,2021:185-190.
[11]ROTHER C,KOLMOGOROV V,BLAKE A.“GrabCut” interactive foreground extraction using iterated graph cuts[J].ACM Transactions on Graphics(TOG),2004,23(3):309-314.
[12]DU Y,LI C,GUO R,et al.Pp-ocr:A practical ultra lightweight ocr system[J].arXiv:2009.09941,2020.
[13]BOSTIK O,KLECKA J.Recognition of CAPTCHA characters by supervised machine learning algorithms[J].IFAC-Papers OnLine,2018,51(6):208-213.
[14]SACHDEV S.Breaking captcha characters using multi-tasklearning cnn and svm[C]//4th International Conference on Computational Intelligence and Networks(CINE 2020).IEEE,2020:1-6.
[15]ZHANG N,EBRAHIMI M,LI W,et al.Counteracting darkWeb text-based CAPTCHA with generative adversarial learning for proactive cyber threat intelligence[J].ACM Transactions on Management Information Systems(TMIS),2022,13(2):1-21.
[16]THOBHANI A,GAO M,HAWBANI A,et al.CAPTCHA re-cognition using deep learning with attached binary images[J].Electronics,2020,9(9):1522.
[17]WU X,DAI S,GUO Y,et al.A machine learning attack againstvariable-length Chinese character CAPTCHAs[J].Applied Intelligence,2019,49:1548-1565.
[18]LUAN S,CHEN C,ZHANG B,et al.Gabor convolutional net-works[J].IEEE Transactions on Image Processing,2018,27(9):4357-4366.
[19]WANG J,QIN J,XIANG X,et al.CAPTCHA recognition based on deep convolutional neural network[J].Math.Biosci.Eng,2019,16(5):5851-5861.
[20]ZHANG X,LIU X,SARKODIE-GYAN T,et al.Development of a character CAPTCHA recognition system for the visually impaired community using deep learning[J].Machine Vision and Applications,2021,32:1-19.
[21]BI X,LIU X.Chinese Character Captcha Sequential SelectionSystem Based on Convolutional Neural Network[C]//International Conference on Computer Vision,Image and Deep Lear-ning(CVIDL 2020).IEEE,2020:554-559.
[22]GIRSHICK R.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448.
[23]CAVNAR W B,TRENKLEJ M.N-gram-based text categorization[C]//3rd Annual Symposium on Document Analysis and Information Retrieval(SDAIR-94).1994:161175.
[24]HU J.Research on Security of Chinese Point-and-Click CAP-TCHA[D].Xi'an:Xidian University,2018.
[25]YOU X.Research on Chinese Character Captcha RecognitionBased on YOLO V2 [D].Chengdu:Chengdu University of Technology,2019.
[26]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788.
[27]LIU W,ANGUELOV D,ERHAND,et al.Ssd:Single shotmultibox detector[C]//Computer Vision-ECCV 2016:14th European Conference,Amsterdam,The Netherlands,Part I 14.Springer International Publishing,2016:21-37.
[28]WANG C Y,BOCHKOVSKIY A,LIAOH Y M.YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.2023:7464-7475.
[29]GE Z,LIU S,WANG F,et al.Yolox:Exceeding yolo series in 2021[J].arXiv:2107.08430,2021.
[30]XU S,WANG X,LV W,et al.PP-YOLOE:An evolved version of YOLO[J].arXiv:2203.16250,2022.
[31]LI C,LI L,JIANG H,et al.YOLOv6:A single-stage object detection framework for industrial applications[J].arXiv:2209.02976,2022.
[32]CHOLLET F.Xception:Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1251-1258.
[33]HOWARD A G,ZHU M,CHEN B,et al.Mobilenets:Efficient convolutional neural networks for mobile vision applications[J].arXiv:1704.04861,2017.
[34]ZHANG X,ZHOU X,LIN M,et al.Shufflenet:An extremelyefficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6848-6856.
[35]KOONCE B.EfficientNet[M]//Convolutional Neural Networks with Swift for Tensorflow:Image Recognition and Dataset Categorization.2021:109-123.
[36]HAN K,WANG Y,TIAN Q,et al.Ghostnet:More featuresfrom cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:1580-1589.
[37]REYNOLDSD A.Gaussian mixture models[J].Encyclopedia of Biometrics,2009,741:659-663.
[38]ZHOU X,YAO C,WEN H,et al.East:an efficient and accurate scene text detector[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:5551-5560.
[39]SHI B,BAI X,YAO C.An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,39(11):2298-2304.
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