Automatic Number Plate Recognition
DOI:
https://doi.org/10.32628/IJSRST2411476Keywords:
Automatic License Plate Recognition, Image Acquisition, License Plate Localization, Character Segmentation, Optical Character Recognition, Identification AccuracyAbstract
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.
Downloads
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
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.