Skip to main content

Automated text detection from big data scene videos in higher education: a practical approach for MOOCs case study


Automated text detection and analysis holds incredible potential for research in higher education. It is challenging because higher education institutes produce an enormous amount and variety of texts, letters, articles, books, reports etc. Futuristic E-learning based education replaces the difficulty of understanding the semantic meaning of the learning content from videos which is most prominent source used by the leaners to acquire knowledge. Therefore, Content Based Video retrieval has become the challenging research area under pattern recognition and computer vision in higher education through Massive Open Online Courses (MOOCs). Text plays a dynamic role in understanding the true meaning of behavior of the video. Hence, it is challenging to detect and identify the text in video due to variable complex background, low contrast, blur, poor illumination, font size, font-style, occlusions. The traditional approach of end-to-end convolution neural network (CNN) performs satisfactory in detecting video text. However, it is also important to deal with the video size, therefore, we have adopted Map Reduce technique to store the video content and utilize it efficiently by parallel computing. Followed by this, we employed novel approach to clean up the video frames to feed to neural network model based on region proposal network (RPN) with CNN by finding appropriate anchor ratios to extract the text candidates. Finally, we train our model with extracted frames to predict for the test videos. The proposed method is evaluated on ICDAR Video text benchmark datasets and few publicly available test datasets to achieve high recall.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15


  1. Adnan, K., & Akbar, R. (2019). An analytical study of information extraction from unstructured and multidimensional big data. Journal of Big Data, 6(1), 1–38. Doi:

  2. Alblawi, A. S., & Alhamed, A. A. (2017). Big data and learning analytics in higher education: Demystifying variety, acquisition, storage, NLP and analytics. In 2017 IEEE Conference on Big Data and Analytics (ICBDA). Doi:,pp.124-129.

  3. Ahmed, I., Din, S., Jeon, G., & Piccialli, F. (2019). Exploring deep learning models for overhead view multiple object detection. IEEE Internet of Things Journal, 1–1. Doi:

  4. Alhazzani, N. (2020). MOOC’s impact on higher education. Social Sciences and Humanities Open, 2(1), 100030. Doi:

  5. Aljawarneh, S., Radhakrishna, V., & Suresh Reddy, G. (2018). Mantra: A novel imputation measure for disease classification and prediction. In Proceedings of the First International Conference on Data Science, E-learning and Information Systems (DATA '18). Association for Computing Machinery, New York, NY, USA, Article 25, 1–5. Doi:

  6. Aljawarneh, S. A. (2019). Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of Computing in Higher Education. Doi:

  7. Aljawarneh, S. A., Radhakrishna, V., & Atwood, J. W. (2020). Ultimate: Unearthing latent time profiled temporal associations. Foundations of Science, 25, 1147–1171.

    Article  Google Scholar 

  8. Al-Rahmi, W., Aldraiweesh, A., Yahaya, N., Kamin, Y., & Zeki, A. (2018). Massive open online courses (MOOCs): Data on higher education. Data in Brief, Elsevier, 22. Doi:

  9. Al-Rahmi, W. M., Alias, N., Othman, M. S., Alzahrani, A. I. Alfarraj, O., Saged, A. A., & Abdul Rahman, N. S. (2018).Use of e-learning by university students in Malaysian higher educational institutions: A case in universiti teknologi Malaysia. IEEE Access, 6, 14268–14276. Doi:

  10. Alexandron, G., Yoo, L. Y., Ruipérez-Valiente, J. A., Lee, S., & Pritchard, D. E. (2019). Are MOOC learning analytics results trustworthy? With fake learners, they might not be! International Journal of Artifcial Intelligence in Education, 29, 484–506.

    Article  Google Scholar 

  11. Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). research trends on big data in marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1–7.

    Article  Google Scholar 

  12. Anshari, M., Alas, Y., & Guan, L. S. (2015). Developing online learning resources: Big data, social networks, and cloud computing to support pervasive knowledge. Education and Information Technologies, 21(6), 1663–1677. Doi:,2015.

  13. Ayed, A. B., Halima, M. B., & Alimi, A. M. (2015). MapReduce based text detection in big data natural scene videos. Procedia Computer Science, 53, 216–223.,pp.216-223

    Article  Google Scholar 

  14. Baran, R., Partila, P., & Wilk, R. (2018). Automated text detection and character recognition in natural scenes based on local image features and contour processing techniques. In International Conference on Intelligent Human Systems Integration, Springer, Dubai, pp. 42–48.

  15. Bhathal, G. S., & Singh, A. (2019). Big data: Hadoop framework vulnerabilities, security issues and attacks”, Array, 100002, 1−8. Doi:

  16. Bhathal, G. S., & Singh, A. (2019). Big data: Hadoop framework vulnerabilities, security issues and attacks. Array, 100002, 1−8. Doi:

  17. Bogarín, A., Cerezo, R., & Romero, C. (2017). “A survey on educational process mining”, Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 8(1), e1230.

    Article  Google Scholar 

  18. Borisyuk, F., Gordo, A., & Sivakumar, V. (2018). Rosetta: large scale system for text detection and recognition in images. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ACM, pp. 71–79

  19. Brooker, A., Corrin, L., de Barba, P. G., Lodge, J., & Kennedy, G. (2018). A tale of two MOOCs: How student motivation and participation predict learning outcomes in different MOOCs. In Australasian Journal of Educational Technology, 34(1), 73–87. Doi:

  20. Chen, L., Zhang, T., Ma, Y. Y., & Zhou, C. (2014). Applied-information technology with distributed text feature extraction method based on mapreduce. Advanced Materials Research, 1046, pp. 444–448

  21. Chen, N. S., Yin, C., Isaias, P., & Psotka, J. (2020). Educational big data: Extracting meaning from data for smart education, Interactive Learning Environments, 28(2), 142–147

  22. Cinbis, R. G., Verbeek, J., & Schmid, C. (2017). Weakly supervised object localization with multi-fold multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(1), 189–203.

    Article  Google Scholar 

  23. Conole, G. (2016). MOOCs as disruptive technologies: strategies for enhancing the learner experience and quality of MOOCs. RED. Revista de Educación a Distancia, Doi:

  24. Daniel, B. K. (ed.), (2017). Big data and learning analytics in higher education. Current Theory and Practices. Doi:

  25. Daniel, B. K. (2017). Overview of big data and analytics in higher education. In Big Data and Learning Analytics in Higher Education. Springer, pp. 1–4.

  26. Devi, M. M., Seetha, M., Raju, S. V., & Rao, D. S. (2019). Detection and tracking of text from video using MSER and SHIFT. Advances in Decision Sciences, Image Processing, Security and Computer Vision, Springer, 4, 719–727.

    Article  Google Scholar 

  27. Ezhilraman, V., & Srinivasan, S. (2018). State of the art in image processing and big data analytics: issues and challenges. International Journal of Engineering and Technology, 7(3.3), 195. Doi:,2018.

  28. Fei, M., & Yeung, D. Y. (2016). Temporal models for predicting student dropout in massive open online courses. Proceedings 15th IEEE International Conference on Data Mining Workshop ICDMW, 2015, 256–263.

    Google Scholar 

  29. Foltýnek, T., Dlabolová, D., Anohina-Naumeca, A., Razı, S., Kravjar, J., Kamzola, L., & Weber-Wulff, D. (2020). Testing of support tools for plagiarism detection. In International Journal of Educational Technology in Higher Education, 17(1). Doi:

  30. Fox, R. (2016). MOOC Impact beyond innovation. In Education in the Asia-Pacific Region: Issues, concerns and prospects, pp. 159–172. Doi:

  31. Gallagher, S. E., & Savage, T. (2016). Comparing learner community behavior in multiple presentations of a Massive Open Online Course. Journal of Computing in Higher Education, 28(3), 358–369. Doi:,2016.

  32. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. Doi:

  33. García-Martínez, C., Cerezo, R., Bermúdez, M., & Romero, C. (2019). Improving essay peer grading accuracy in massive open online courses using personalized weights from student’s engagement and performance. Journal of Computer Assisted Learning.

    Article  Google Scholar 

  34. Gil-Jaurena, I., & Kucina Softic, S. (2016). Aligning learning outcomes and assessment methods: A web tool for e-learning courses. International Journal of Educational Technology in Higher Education, 13(1). Doi:,2016.

  35. Gupta, A., Vedaldi, A., & Zisserman A. (2016). Synthetic data for text localization in natural images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 2315–2324

  36. Gupta, K. P. (2019). Investigating the adoption of MOOCs in a developing country. Interactive Technology and Smart Education. Doi:

  37. Ha, I., Back, B., & Ahn, B. (2015). MapReduce functions to analyze sentiment information from social big data. International Journal of Distributed Sensor Networks, 11(6), 417502. Doi:

  38. Hamad, M. M. (2017). Pros & Cons of using blackboard collaborate for blended learning on students learning outcomes. Higher Education Studies, 7(2), 7.

    Article  Google Scholar 

  39. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778

  40. He, W., Zhang, X., Yin, F., & Liu, C. (2017). Deep Direct Regression for Multi-Oriented Scene Text Detection”, IEEE International Conference on Computer Vision (ICCV), Vinice, Italy, pp. 745–753

  41. Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers and Education, 98, 157–168.

    Article  Google Scholar 

  42. Howarth, J. P., D’Alessandro, S., Johnson, L., & White, L. (2016). Learner motivation for MOOC registration and the role of MOOCs as a university ‘taster’. International Journal of Lifelong Education, 35(1), pp.74–85. Doi:

  43. Hu, H., C. Zhang, C., Luo, Y., Wang, Y., Han, J., & Ding, E. (2017).WorkSup: Exploiting word annotations for character based text detection. IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 4940–4949

  44. Huang., L ., Zhang, J., Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation efect of course difculty. International Journal of Information Management, 37(2), 84–91. Doi:,2017.

  45. Jie, Z., Wei, Y., Jin, X., Feng, J., & Liu, W. (2017). Deep self-taught learning for weakly supervised object localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1377–1385

  46. Khalil, M., & Ebner, M. (2016). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): the use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1): 114–132. Doi:,2016.

  47. Kintu, M. J., Zhu, C., & Kagambe, E. (2017). Blended learning effectiveness: The relationship between student characteristics, design features and outcomes. In International Journal of Educational Technology in Higher Education, 14(1). Doi:,2017.

  48. Kong, Y., Huang, J., Huang, S., Wei, Z., & Wang, S. (2019). Learning spatiotemporal representations for human fall detection in surveillance video. Journal of Visual Communication and Image Representation, 59, 215–230.

    Article  Google Scholar 

  49. Krizkova, S., Tomaskova, H., & Gavalec, M. (2016). Preference comparison for plagiarism detection systems. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Doi:,pp.1760-1767.

  50. Lara, J. A., Aljawarneh, S., & Pamplona, S. (2019). Special issue on the current trends in E-learning Assessment. Journal of Computing in Higher Education. Doi:

  51. Li, K. (2019). MOOC learners’ demographics, self-regulated learning strategy, perceived learning and satisfaction: A structural equation modeling approach. Computers and Education, 132, 16–30.

    Article  Google Scholar 

  52. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, & Berg, A. C. (2016). SSD: Single Shot Multibox Detector. In European Conference on Computer Vision, Springer, Netherlands, pp. 31–37.

  53. Liu, Y., & Jin, L. (2017). Deep matching prior network: Toward tighter multi-oriented text detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 3454–3461

  54. Liu, M., Zhu, M. (2018).Mobile video object detection with temporally-aware feature maps. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 5686–5695

  55. Ijaz Baig, M., Shuib, L., & Yadegaridehkordi E. (2020). Big data in education: a state of the art, limitations, and future research directions. In: International Journal of Educational Technology in Higher Education, Springer

  56. Lu, W., Sun, H., CHhu, J., Huang, X., & Yu, J. (2018). A novel approach for video text detection and recognition based on a corner response feature map and transferred deep convolutional neural nework. IEEE, 6, 40198–40211

  57. Ma, J., Shao, W., Ye, H., Wang, L., Zheng, Y., & Xue, X. (2018). Arbitrary-oriented scene text detection via rotation proposals. IEEE Transactions on Multimedia, pp. 3111–3122

  58. Ma, J., Shao, W., Ye, H., Wang, L., Zheng, Y., & Xue, X. (2018). Arbitrary-oriented scene text detection via rotation proposals. IEEE Transactions on Multimedia, pp. 3111–3122

  59. Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems33(1), pp.107–124.

  60. Mangaroska, K., & Giannakos, M. N. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies.

    Article  Google Scholar 

  61. Matthew, W. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics 34, 77–84.

  62. Milligan, C., & Littlejohn, A. (2017). Why study on a MOOC? The motives of students and professional. In The International Review of Research in Open and Distributed Learning, 18(2). Doi:,2017.

  63. Monllaó Olivé, D., Huynh, D. Q., Reynolds, M., & Wiese, M. D. (219). A supervised learning framework: Using assessment to identify students at risk of dropping out of a MOOC. Journal of Computing in Higher Education. Doi:,2019.

  64. Mirza, A., Fayyaz, M., Seher, Z., & Siddiqi, I. (2018). Urdu caption text detection using textural features. In Proceedings of the 2nd Mediterranean Conference on Pattern Recognition and Artificial Intelligence, ACMRabat, pp. 70–75.

  65. Moore, R. L., & Wang, C. (2020). Influence of learner motivational dispositions on MOOC completion. Journal of Computing in Higher Education.

    Article  Google Scholar 

  66. Murino, V., Gong, S., Loy, C. C., & Bazzani, L. (2017). Image and video understanding in big data. Computer Vision and Image Understanding, 156, 1–3.

    Article  Google Scholar 

  67. Ning, C., Zhou, H., Song, Y., & Tang, J. (2017). Inception single shot multibox detector for object detection. In IEEE International Conference on Multimedia and Expo Workshops (ICMEW), China, pp. 549–554.

  68. Oh, E. G., Chang, Y., & Park, S. W. (2019). Design review of MOOCs: Application of e-learning design principles. Journal of Computing in Higher Education.

    Article  Google Scholar 

  69. Oi, M., Yamada, M., Okubo, F., Shimada, A., & Ogata, H. (2017). Reproducibility of findings from educational big data. In Paper presented at the proceedings of the Seventh International Learning Analytics and Knowledge Conference, pp. 536–537, New York: ACM. Doi:,2017.

  70. Palmero, R., Colomo-Magaña, J. E., Ríos-Ariza, J. M., & Gómez-García M. (2020). Big data in education: Perception of training advisors on its use in the educational system. Social Sciences, 9(4), 53. Doi:,2020.

  71. Pan, L., Wang, X., Li, C., Li, J., & Tang, J. (2017). Course concept extraction in MOOCs via embedding-based graph propagation. In Proceedings of the The 8th International Joint Conference on Natural Language Processing, pp. 875–884. Taipei, Taiwan, November 27–December 1, 2017.

  72. Poellhuber, B., Roy, N., & Bouchoucha, I. (2019). Understanding participant’s behaviour in massively open online courses. The International Review of Research in Open and Distributed Learning, 20(1). Doi:

  73. Pozón-López, I., Higueras-Castillo, E., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2020). Perceived user satisfaction and intention to use massive open online courses (MOOCs). Journal of Computing in Higher Education. Doi:,2020.

  74. Qin, H., Zhang, H., & Wang, H. (2019). An algorithm for scene text detection using multibox and semantic segmentation. MDPI, Applied Science, vol. 9

  75. Qiu, J., Tang, J., Liu, T. X., Gong, J., Zhang, C., Zhang, Q., & Xue, Y. (2016). Modeling and predicting learning behavior in MOOCs. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, ACM, pp. 93–102.

  76. Rabin, E., Kalman, Y. M., & Kalz, M. (2019). An empirical investigation of the antecedents of learnercentered outcome measures in MOOCs. International Journal of Educational Technology in Higher Education, 16(1), 14.

    Article  Google Scholar 

  77. Radhakrishna, V., Aljawarneh, S. A., Janaki, V., & Kumar, P. V. (2017a). Looking into the possibility for designing normal distribution based dissimilarity measure to discover time profiled association patterns. International Conference on Engineering and MIS (ICEMIS), 2017, 1–5.

    Article  Google Scholar 

  78. Radhakrishna, V., Kumar, P. V., Janaki, V., & Cheruvu, A. (2017b). A dissimilarity measure for mining similar temporal association patterns. IADIS International Journal on Computer Science and Information Systems, 12(1), 126–142.

    Google Scholar 

  79. Radhakrishna, V., Kumar, P. V., & Janaki, V. (2017c). Design and analysis of similarity measure for discovering similarity profiled temporal association patterns. IADIS International Journal on Computer Science and Information Systems, 12(1), 45–60.

    Google Scholar 

  80. Radhakrishna, V., Kumar, P. V., & Janaki, V. (2017d). Normal distribution based similarity profiled temporal association pattern mining (N-SPAMINE). Database Systems Journal, 7(3), 22–33.

    Google Scholar 

  81. Radhakrishna, V., Kumar, P. V., & Janaki, V. Krishna Sudarsana A. (2018). Z-space similarity measure. In Proceedings of the fourth international conference on engineering and MIS 2018 (ICEMIS '18). Association for Computing Machinery, New York, NY, USA, Article 44, 1–4. Doi:

  82. Radhakrishna, V., Veereswara Kumar, P. & Janaki, V. (2018). SRIHASS—A similarity measure for discovery of hidden time profiled temporal associations. Multimedia Tools Applications 77, 17643–17692. Doi:

  83. Radhakrishna, V., Aljawarneh S., Kumar, P. V., Janaki, V., & Cheruvu, A. (2019). Discovery of time profiled temporal patterns. In Proceedings of the 5th International Conference on Engineering and MIS (ICEMIS '19). Association for Computing Machinery, New York, NY, USA, Article 27, 1–5. Doi:

  84. Reidenberg, J. R., & Schaub, F. (2020). Achieving big data privacy in education. Theory and Research in Education, 147787851880530. Doi:

  85. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified real-time object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, pp. 779–788.

  86. Redondo, T., & Sandoval, A. (2016). Text analytics: The convergence of big data and artificial intelligence. In International Journal of Interactive Multimedia and Artificial Intelligence, 3(6), pp. 57–64. Doi:

  87. Ren, S., He, K., Girishick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 39(6), 1137–1149.

    Article  Google Scholar 

  88. Rizvi, S., Rienties, B., Rogaten, J., & Kizilcec, R. F. (2019). Investigating variation in learning processes in a FutureLearn MOOC. Journal of Computing in Higher Education. Doi:

  89. Rodriguez, P., Ortigosa, A., & Carro, R. M. (2012). Extracting emotions from texts in E-learning environments. In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 887–892.

  90. Sayahi, S., & Ben Halima, M. (2014). An intelligent and robust multi-oriented image scene text detection. In The 6th International Conference of Soft Computing and Pattern Recognition (IEEE SoCPaR’2014). Ed. IEEE. Tunis, Tunisia: IEEE, pp.418–422.

  91. Shang, H.-F. (2018). An investigation of plagiarism software use and awareness training on English as foreign language (EFL) students. Journal of Computing in Higher Education. Doi:

  92. Shi, M., & Ferrari, V. (2016). Weakly supervised object localization using size estimates. In European Conference on Computer Vision. Springer, pp. 105–121

  93. Shi-Yong, Z., Jiang, S., Yue, X., Pu, R., & Li, B. (2019). Application research of an innovative online education model in big data environment. iJET 14: pp. 125–38

  94. Sigalov, S. E., & Nachmias, R. (2017). Wikipedia as a platform for impactful learning: A new course model in higher education. Education and Information Technologies, 22(6), 2959–2979.

    Article  Google Scholar 

  95. Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR), San Diego, CA, USA.

  96. Sujatha, R., & Kavitha, D., (2018). Learner retention in MOOC environment: Analyzing the role of motivation, self-efcacy and perceived effectiveness. International Journal of Education and Development Using Information and Communication Technology, 14(2), 62–74.

  97. Tian, Z., Huang, W., He, T., He, P., & Qiao, Y. (2016). Detecting text in natural image with connectionist text proposal network. European Conference on Computer Vision, Springer, Netherlands, 9912, 56–72.

    Google Scholar 

  98. Tian, S., Yin, X. C., Su, Y., & Hao, H. W. (2018). A unified framework for tracking based text detection and recognition from web videos”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3), 542–554.

    Article  Google Scholar 

  99. Wang, S., & Kelly, W. (2017). Video-based big data analytics in cyberlearning. Journal of Learning Analytics, 4(2), 36–46. Doi:

  100. Wang, L., Wang, Y., Shan, S., & Su, F. (2018). Scene text detection and tracking in video with background cues. International Conference on Multimedia Retrieval (ICMR), ACM, pp.160–168

  101. Wang, J., Wang, X., & Liu, W. (2018). Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning. In 24th International Conference on Pattern Recognition (ICPR), pp. 2416–2421. Doi:

  102. Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable genetic programming: Integrating learning analytics, educational data mining and theory. Elsevier, Computers in Human Behavior, 47, 168–181.

    Article  Google Scholar 

  103. Xing, W. (2018). Exploring the influences of MOOC design features on student performance and persistence. Distance Education, pp. 1–16. Doi:

  104. Xu, Y., Shan, S., Qiu, Z., Jia, Z., Shen, Z., Wang, Y., Shi, M., Eric, I., & Chang, C. (2018). End-to-end subtitle detection and recognition for videos in east asian languages via cnn ensemble. Signal Processing Image Communication, 60, 131–143.

    Article  Google Scholar 

  105. Xuan Nguyen, P., Wang, K., & Belongie, S. (2014). Video text detection and recognition: Dataset and benchmark. In IEEE Winter Conference on Applications of Computer Vision. pp. 776–783. Doi:,2014.

  106. Xuefang, X., Lei, Y., & Li, Z. (2019). An incorrect data detection method for big data cleaning of machinery condition monitoring. IEEE Transactions on Industrial Electronics 67, 2326–2336

  107. Ye, C., Kinnebrew, J. S., Biswas, G., Evans, B. J., Fisher, D. H., Narasimham, G., & Brady, K. A. (2015). Behavior prediction in MOOCs using higher granularity temporal information. In Proceedings of the second (2015) ACM conference on Learning@Scale, pp. 335–338, New York, NY: ACM. Doi:

  108. Ye, Y., Zhu, S., Wang, J., Du, Q., Yang, Y., Tu, D., & Luo, J. (2018). A unified scheme of text localization and structured data extraction for joint OCR and data mining. In 2018 IEEE International Conference on Big Data (Big Data), pp. 2373–2382, Doi:

  109. Yin, C., & Hwang, G. J. (2018). Roles and strategies of learning analytics in the e-publication era. Knowledge Management and E-Learning, 10(4), 455–468.

  110. Zhong, Z., Jin, L., & Huang, S. (2017). DeepText: A new approach for proposal generation and text detection in natural images. In International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, New Orleans, LA, USA

  111. Zhong, H., & Ma, Y. (2018). Fast and Robust text detection in MOOCs videos. In Proceedings of the 2018 International Conference on Distance Education and Learning—ICDEL 1, 98–102. Doi:

  112. Zhao, Y., Lofi, C., & Hauff, C. (2019). Scalable mind-wandering detection for MOOCs: A webcam-based approach. In Lecture Notes in Computer Science, pp. 330–344. Doi:

  113. Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., & Liang, J. (2017). EAST: An efficient and accurate scene text detector. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 2642–2651.

Download references


The author(s) received no specific funding for this study.

Author information



Corresponding author

Correspondence to Mortha Manasa Devi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Manasa Devi, M., Seetha, M. & Viswanadha Raju, S. Automated text detection from big data scene videos in higher education: a practical approach for MOOCs case study. J Comput High Educ (2021).

Download citation


  • Text detection
  • Localization
  • CNN
  • Pixel pair coordinates
  • RPN
  • Anchors
  • Regions
  • E-learning
  • Map reduce
  • Big data
  • Higher education
  • MOOCs