Automatic Number Plate Recognition

Authors

  • Swanand Joshi Department of Computer Engineering, ZCOER, Pune, Maharashtra, India Author
  • Pramod Jejure Department of Computer Engineering, ZCOER, Pune, Maharashtra, India Author
  • Chatrasal Jadhav Department of Computer Engineering, ZCOER, Pune, Maharashtra, India Author
  • Vishal Jankar Department of Computer Engineering, ZCOER, Pune, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRST2411476

Keywords:

Automatic License Plate Recognition, Image Acquisition, License Plate Localization, Character Segmentation, Optical Character Recognition, Identification Accuracy

Abstract

Automatic license plate recognition (ANPR) systems have become suitable for various applications, including traffic monitoring, law enforcement, and toll collection. This paper completes the study on automatic license plate recognition (ANPR) systems that use advanced imaging technology and machine learning algorithms to achieve accuracy in license plate verification and validation. The preparation process is adopted in various ways: image acquisition, preprocessing, location plate, character segmentation, and optical character recognition (OCR). The system, which integrates deep learning models for extraction and classification, performs better in different environments. Experimental results show that the identification accuracy of the proposed ANPR exceeds 95%, demonstrating its potential in practical applications. In addition, this paper also discusses the problems encountered in ANPR implementation, including changes in plate design, illumination, and shading, and provides suggestions for future research to improve stability and efficiency. Keywords: automatic plate recognition, image processing, machine learning, optical behavior recognition, deep learning, vehicle tracking, driving license inspection.

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References

M.K.Davy, P. J. Banda, and A. Hamweendo, ‘‘Automatic vehicle number plate recognition system,’’ Phys. Astron. Int. J., vol. 7, no. 1, pp. 69–72, Mar. 2023. DOI: https://doi.org/10.15406/paij.2023.07.00286

A. V. Burkpalli, A. Joshi, A. B. Warad, and A. Patil, ‘‘Automatic number plate recognition using TensorFlow and easyocr,’’ Int. Res. J. Mod. Eng. Technol. Sci., vol. 4, no. 9, pp. 493–501, 2022.

S. Rafique, S. Gul, K. Jan, and G. M. Khan, ‘‘Optimized real-time parking management framework using deep learning,’’ Exp. Syst. Appl., vol. 220, Jun. 2023, Art. no. 119686. DOI: https://doi.org/10.1016/j.eswa.2023.119686

M. Jin, C. Sun, and Y. Hu, ‘‘An intelligent traffic detection approach for vehicles on highway using pattern recognition and deep learning,’’ Soft Compute., vol. 27, no. 8, pp. 5041–5052, Apr. 2023. DOI: https://doi.org/10.1007/s00500-022-07375-3

M. Sankaranarayanan, C. Mala, and S. Mathew, ‘‘Efficient vehicle detection for traffic video-based intelligent transportation systems applications using recurrent architecture,’’ Multimedia Tools Appl., vol. 82, no. 25, pp. 39015–39033, Oct. 2023. DOI: https://doi.org/10.1007/s11042-023-14812-4

T. Azfar, J. Li, H. Yu, R. L. Cheu, Y. Lv, and R. Ke, ‘‘Deep learning-based computer vision methods for complex traffic environments perception: A review,’’ Data Sci. for Transp., vol. 6, no. 1, p. 1, Apr. 2024. DOI: https://doi.org/10.1007/s42421-023-00086-7

V. Rajyalakshmi and K. Lakshmanna, ‘‘Detection of car parking space by using hybrid deep Dense Net optimization algorithm,’’Int.J.Netw.Manag., vol. 34, no. 1, p. e2228, Jan. 2024. DOI: https://doi.org/10.1002/nem.2228

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Published

29-04-2024

Issue

Section

Research Articles

How to Cite

Automatic Number Plate Recognition. (2024). International Journal of Scientific Research in Science and Technology, 11(5), 439-448. https://doi.org/10.32628/IJSRST2411476

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