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Bitmap Image Recognition with Neural Networks

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Logistics, finance, science, and trade are just some of the areas that require computer vision technology, which includes number recognition. The need to recognize numbers in images or photographs is found in tasks such as recognizing car numbers, reading values from paper bills, recognizing object identification numbers, and reading credit card numbers. The development of an online application for recognition numbers in bitmap images using machine training technologies, namely an artificial neural network based on the class of neural networks perceptron, is an actual task.
Słowa kluczowe
Twórcy
  • Lviv Polytechnic National University
  • Lviv Polytechnic National University
  • Lviv Polytechnic National University
Bibliografia
  • 1. He J., Li X., Xiao J. 2020. CNN-based image recognition and processing technology development of airborne display and control system, Journal of Physics: Conference Series. 1684 (1), art. no. 012108.
  • 2. Yu W., Li Y., Peng H., Zhang L. 2020. Image iterative method for handwritten Chinese character recognition, Journal of Physics: Conference Series, 1684 (1), art. no. 012101.
  • 3.Ilyuhin S. A., Sheshkus A. V., Arlazarov V. L. 2020. Recognition of images of Korean characters using embedded networks, Proceedings of SPIE, The International Society for Optical Engineering, 11433, art. no. 1143311.
  • 4. Kim C.-M., Hong E.J., Chung K., Park R.C. 2020. Line-segment feature analysis algorithm using input dimensionality reduction for handwritten text recognition, Applied Sciences (Switzerland), 10 (19), art. no. 6904, pp. 1-17.
  • 5. Belay B., Habtegebrial T., Meshesha M., Liwicki M., Belay G., Stricker D. 2020. Amharic ocr: An end-to-end training, Applied Sciences (Switzerland), 10 (3), art. no. 1117.
  • 6. Oni O. J., Asahiah F. O. 2020. Computational modelling of an optical character recognition system for Yorùbá printed text images, Scientific African, 9, art. no. e00415.
  • 7. Raphael A., Dubinsky Z., Iluz D., Netanyahu N. S. 2020. Neural network recognition of marine benthos and corals, Diversity, 12 (1), art. no. 29.
  • 8. Zhang C., Yue P., Di L., Wu Z. 2018. Automatic identification of center pivot irrigation systems from landsat images using convolutional neural networks, Agriculture (Switzerland), 8 (10), art. no. 147.
  • 9. Yang W., Liu, Q., Wang S., Cui Z., Chen X., Chen L., Zhang N. 2018. Down image recognition based on deep convolutional neural network, Information Processing in Agriculture, 5 (2), pp. 246-252.
  • 10. Rybchak Z., Basystiuk O. 2017. Analysis of computer vision and image analysis technics. ECONTECHMOD, vol. 6. No. 2. Pp. 79–84.
  • 11. Shumeiko A., Smorodskyi V. 2017. Discrete trigonometric transform and its usage in digital image processing. ECONTECHMOD, vol. 06, No. 4, pp. 21-26.
  • 12. Kaminsky R., Borovets Ya. 2017. Model of operator activity in computer systems image processing. ECONTECHMOD, vol. 6. No. 2, pp. 9–14.
  • 13. Veres O., Kis Ya., Kugivchak V., Rishniak I. 2018. Development of a Reverse-search System of Similar or Identical Images. ECONTECHMOD, vol. 07, No. 2, pp. 23-30.
  • 14. Boyko N., Sokil N. 2017. Building computer vision systems using machine training algorithms, ECONTECHMOD. vol. 6, No. 2, pp. 15–20.
  • 15. Chen P.-J., Yang S.-Y., Wang C.-S., Muslikhin M., Wang M.-S. 2020. Development of a chinese chess robotic system for the elderly using convolutional neural networks, Sustainability (Switzerland), 12 (10), art. no. 3980.
  • 16. Paulino D., Reis A., Paredes H., Fernandes H., Barroso, J. 2019. Usage of artificial vision cloud services as building blocks for blind people assistive systems, International Journal of Recent Technology and Engineering, 8 (2 Special Issue 10), pp. 453-458.
  • 17. Matvieyeva Ye. 2016. Algoriths: development and application, Classics of Computers Science, StPb, 800 p.
  • 18. Coreman T., et al. 2019. Algorithms: design and analysis, Dialectica, StPb, 1328 p.
  • 19. Rafaello C. 2013. Graphics on JavaScript, StPb, 272 p.
  • 20. Shestakevych T., Pasichnyk V., Kunanets N., Medykovskyy M., Antonyuk N. 2018. The content web-accessibility of information and technology support in a complex system of educational and social inclusion, 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2018 - Proceedings, 1, art. no. 8526691, pp. 27-31.
  • 21. Simpson K. 2015. You don’t know JS: UP & Going, O’Reilly Media, Inc, 224 p.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-76eb7648-4775-4b62-953c-5308ce478d4f
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