Automatic License Plate Recognition System

References

[1] Siah, Y. K Haur, T. Y. Khalid, M. and Ahmad, T. (2012). Vehicle Licence Plate Recognition by Fuzzy Artmap Neural Network, obtained from www.researchgate.com, on February, 16 20203.

[2] Manana, M. Tu, C. and Owolawi, P. A.(2021). Edge-Based Licence-Plate Template Matching for Identifying Similar Vehicles, Vehicles 2021, 3, 646–660. https://doi.org/10.3390/vehicles3040039.

[3] Wu, B.-F., Lin S.-P. and Chiu, C.-C.(2007). Extracting characters from real vehicle licence plates out-of-doors, IET Computer Vision · April 2007. DOI: 10.1049/iet-cvi:20050132. Source: IEEE Xplore.

[4] Baranidharan V., Varadharajan, K. Sudhakar K., and Lokesh, N.V. (2018). Bounding Box Method Based Accurate Vehicle Number Detection and Recognition for High Speed Applications, ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, NOVEMBER 2018, VOLUME: 09, ISSUE: 02, ISSN: 0976-9102 (ONLINE), DOI: 10.21917/ijivp.2018.0264.

[5] Boby, A. and Brown, D. (2022). Improving Licence Plate Detection using Generative Adversarial Networks. Pattern Recognition and Image Analysis · April 2022 DOI: 10.1007/978-3-031-04881-4_47.

[6] Pirgazi, J. Kallehbasti , M. M. P. andSorkhi, A. G.(2022). An End-to-End Deep Learning Approach for Plate Recognition in Intelligent Transportation Systems. Wireless Communications and Mobile Computing Volume 2022, Article ID 3364921, 13 pages. https://doi.org/10.1155/2022/3364921.

[7] Tiwari, B. Sharma, A. Singh, M. G. and Rathi, B. (2022). Automatic Vehicle Number Plate Recognition System using Matlab. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 11, Issue 4, Ver. II (Jul.-Aug .2016), PP 10-16. www.iosrjournals.org.

[8] Pattanaik, A. andBalabantaray, R. C. (2022). Licence Plate Recognition System for Intelligence Transportation Using BR-CNN. Chapter · March 2022 DOI: 10.1007/978-981-16-8403-6_60

[9] Lazrus, A. Choubey, S. and Sinha G.R. (2011). An Efficient Method of Vehicle Number Plate Detection and Recognition. International Journal of Machine Intelligence. ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 3, 2011, pp-134-137. Available online at http://www.bioinfo.in/contents.php?id=31

@article{1704227,
      author = {Rosaline A. Eke, Stella I. Orakwue},
      title = {Automatic License Plate Recognition System},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {10},
      pages = {182-189},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704227.pdf},
      abstract = {A fast, accurate and reliable vehicle plate recognition system is a useful tool that will help stem the rising tide of vehicle theft and traffic violations perpetuated in society and improve the overall safety of our roads.Automatic License Plate Recognition (ALPR) system is a real-time embedded system that recognizes the license plate of vehicles.The model proposed in this work for implementing the ALPR system uses an open-source C/C++ library called Open ALPR, based on OpenCV, Leptonica and Tesseract-OCR. The goal of the system is to use image processing to identify number plates on images of vehicles and also in a video stream. The hardware is implemented with Raspberry Pi zero, and a Pi camera module. The operating system which would run on the Raspberry pi is known as the Raspbian Jessie Pixel, a Linux distribution built for the Raspberry Pi which is more or less a combination of Raspberry and Debian. Results showed that the model has an accuracy of 97.6% for image extraction, 96% for character segmentation and 98.8% for character recognition. The overall systemperformance whichis a product of all unit?s accuracy rates (Extraction of plate region, segmentation of characters and recognition of characters) shows an improvement from existing models.},
      keywords = {Automatic License Plate Recognition, Character segmentation, Optical Character Recognition, OpeCV, Leptonica},
      month = {April},
  }