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Object Detection Based Software System for Automatic Evaluation of Cursogramas Images

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Applied Informatics (ICAI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1455))

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Abstract

The aim of this work is to describe the tasks performed to carry out the development of a software system capable of detecting and recognizing the symbols of Cursogramas in images by using a Deep Learning model that has been trained from scratch. In this way, we seek to assist teachers of an undergraduate subject to automatically evaluate diagrams made as part of the practical exercise of their students. For this purpose, in addition to having carried out a process of understanding the problem and identifying the available data, tasks of technology selection and construction of each of the components that are part of the system are also carried out. Therefore, although the problem domain belongs to the field of university education, this work is more related to the engineering and technological aspect of the application of Artificial Intelligence to solve complex problems.

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References

  1. Pollo-Cattaneo, M.F.: The Organization and its Information Systems, 1st edn. Editorial CEIT, Buenos Aires (2017). ISBN 978-987-1978-36-6

    Google Scholar 

  2. IRAM (1974). IRAM 34503: Administrative procedures. General guidelines for the design of forms for graphic representation, 1st edn. Argentine Institute for Standardization and Certification (in force since 03/05/1974)

    Google Scholar 

  3. UTN-FRBA: Analytical program of the chair ‘Systems and Organizations’ - Plan 2008 (2008). https://tinyurl.com/y7xx33y5. Accessed May 2021

  4. UTN: Curricular design of the Information Systems Engineering career - Plan 2008. Ordinance UTN-1150 (2008). https://tinyurl.com/yb22zm2q. Accessed June 2021

  5. Cohen, P.R., Feigenbaum, E.A.: The Handbook of Artificial Intelligence, vol. 3. Butterworth-Heinemann (2014)

    Google Scholar 

  6. Vegega, C., Pytel, P., Pollo-Cattaneo, M.F.: Elicitation of requirements for the construction of predictive models based on intelligent systems in education. In: Proceedings of XXV CACIC 2019, pp. 980–989 (2019). ISBN 978-987-688-377-1

    Google Scholar 

  7. Chair SyO: Template of Symbols for use in Cursogramas to be used in the subject ‘Systems and Organizations’ of UTN FRBA (2020). https://tinyurl.com/y6jdnfy3. Accessed June 2021

  8. Outair, A., Lyhyaoui, A., Tanana, M.: Towards an automatic evaluation of UML class diagrams by graph transformation. Int. J. Comput. Appl. 95(21), 36–41 (2014). https://doi.org/10.5120/16721-7063

    Article  Google Scholar 

  9. Lino, A. D.P., Rocha, A.: Automatic evaluation of ERD in e-learning environments. In: 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–5 (2018)

    Google Scholar 

  10. Schäfer, B., Keuper, M., Stuckenschmidt, H.: Arrow R-CNN for handwritten diagram recognition. Int. J. Doc. Anal. Recogn. (IJDAR) 24(1–2), 3–17 (2021). https://doi.org/10.1007/s10032-020-00361-1

    Article  Google Scholar 

  11. Anwer, S., El-Attar, M.: An evaluation of the statechart diagrams visual syntax. In: 2014 International Conference on Information Science & Applications (ICISA), pp. 1–4 (2014)

    Google Scholar 

  12. Vachharajani, V., Pareek, J., Gulabani, S.: Effective label matching for automatic evaluation of use-case diagrams. In: 2012 IEEE 4th International Conference on Technology for Education, pp. 172–175 (July 2012)

    Google Scholar 

  13. Elyan, E., Jamieson, L., Ali-Gombe, A.: Deep learning for symbols detection and classification in engineering drawings. Neural Netw. 129, 91–102 (2020)

    Article  Google Scholar 

  14. Altun, O., Nooruldeen, O.: SKETRACK: stroke-based recognition of online hand-drawn sketches of arrow-connected diagrams and digital logic circuit diagrams. Sci. Program. 2019, 1–17 (2019). https://doi.org/10.1155/2019/6501264

    Article  Google Scholar 

  15. Alpaydin, E.: Introduction to Machine Learning. MIT Press (2014)

    Google Scholar 

  16. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  17. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  18. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2017)

    Google Scholar 

  19. Zhou, D.X.: Universality of deep convolutional neural networks. Appl. Comput. Harmon. Anal. 48(2), 787–794 (2020)

    Article  MathSciNet  Google Scholar 

  20. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

  21. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)

    Google Scholar 

  22. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  23. Huang, J., Rathod, V., et al.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7310–7311 (2017)

    Google Scholar 

  24. Khandelwal, R.: COCO and Pascal VOC data format for Object detection. Medium (2019). https://tinyurl.com/y5fgyb9n. Accessed June 2021

  25. Smith, R.W.: History of the Tesseract OCR engine: what worked and what didn’t. In: Document Recognition and Retrieval XX, vol. 8658, p. 865802. International Society for Optics and Photonics (February 2013)

    Google Scholar 

  26. GEMIS: Randomly Generated Cases with its Results (2021). https://tinyurl.com/7k8mhrtw. Accessed June 2021

  27. Bovik, A.C. (ed.): The Essential Guide to Image Processing. Academic Press (2009)

    Google Scholar 

  28. Velosa, F., Florez, H.: Edge solution with machine learning and open data to interpret signs for people with visual disability. CEUR Workshop Proc. 2714, 15–26 (2020)

    Google Scholar 

  29. Zuluaga, J.Y., Yepes-Calderon, F.: Tensor domain averaging in diffusion imaging of small animals to generate reliable tractography. ParadigmPlus 2(1), 1–19 (2021)

    Google Scholar 

  30. Espinosa, C., Garcia, M., Fernando Yepes-Calderon, J., McComb, G., Florez, H.: Prostate cancer diagnosis automation using supervised artificial intelligence. a systematic literature review. In: Florez, H., Misra, S. (eds.) ICAI 2020. CCIS, vol. 1277, pp. 104–115. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61702-8_8

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Grupo de Estudio en Metodologías de Ingeniería en Software (GEMIS), Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Buenos Aires, Argentina

    Pablo Pytel, Matías Almad, Rocío Leguizamón, Cinthia Vegega & Ma Florencia Pollo-Cattaneo

Authors
  1. Pablo Pytel
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  2. Matías Almad
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  3. Rocío Leguizamón
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  4. Cinthia Vegega
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  5. Ma Florencia Pollo-Cattaneo
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Corresponding authors

Correspondence to Pablo Pytel , Cinthia Vegega or Ma Florencia Pollo-Cattaneo .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogotá, Colombia

    Hector Florez

  2. Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Buenos Aires, Argentina

    Ma Florencia Pollo-Cattaneo

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Pytel, P., Almad, M., Leguizamón, R., Vegega, C., Pollo-Cattaneo, M.F. (2021). Object Detection Based Software System for Automatic Evaluation of Cursogramas Images. In: Florez, H., Pollo-Cattaneo, M.F. (eds) Applied Informatics. ICAI 2021. Communications in Computer and Information Science, vol 1455. Springer, Cham. https://doi.org/10.1007/978-3-030-89654-6_4

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  • DOI: https://doi.org/10.1007/978-3-030-89654-6_4

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  • Print ISBN: 978-3-030-89653-9

  • Online ISBN: 978-3-030-89654-6

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