Comparing and Analysis of Various Automated Model for Number Plate Detection

Authors

  • Kushagra Mangal
  • Pawan Kumar Patidar

DOI:

https://doi.org/10.37591/rrjoesa.v9i2.836

Keywords:

OpenCV, Tesseract, number plate detection, image processing, Python tools for neural network

Abstract

In this paper, we analyze the results by comparing different methods to identify the number plate and it is more suitable for detecting the number plate involved in accuracy and image processing than any other model. This paper presents the usage of pictures for physical changes. The page refers to the different advances required to extract content from any picture record and makes a different book document, including information removed from the picture record. This method is used for reading the vehicle number plate, which incorporates a vehicle picture or moving picture. This is a significant achievement in innovation that is useful for traffic officers. There are various sources of image processing available and that can be used to bring you different stages of image processing and filtering. OpenCV tool that is used for image processing is used for extraction of the text from the image used by Tesseract, the various images are tested at different levels in image processing for providing better and efficient results. After using the image printing step, the output text file is filtered and formatted. Efficiency of number plate extraction is 80%.

References

Shan Du, Mahmoud Ibrahim, Mohamed Shehata, Wael Badawy. Automatic license plate recognition: a state-of-the-art review, IEEE Trans Circ Syst Video Technol. 2013; 23(2): 311–325p.

Anthony Kay (2007). Tesseract: An Open-Source Optical Character Recognition Engine. Available from https://www.linuxjournal.com/article/9676

Hanit Karwal, Akshay Girdhar. Vehicle number plate detection system for Indian vehicles. 2015 IEEE International Conference on Computational Intelligence & Communication Technology. 13–14 Feb. 2015; Ghaziabad, India, IEEE; 2015.

Yasuo Ogiuchi, Masakatsu Higashikubo, Sakda Panwai, Elarin Luenagvilai, Automatic license plate detection and recognition in Thailand, SEI Technical Rev. 2014; 184: 34–39p.

Kay Anthony. The RedMonk Programming Language Rankings: June 2015–tecosystems. Redmonk.com. 1 July 2015. Retrieved 10 September 2015.

Sharma-Jitendar, A hybrid technique for license plate recognition based on feature selection of wavelet transform and artificial neural network, IEEE, 2014.

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Published

2021-12-22

Issue

Vol. 9 No. 2 (2021)

Section

Articles

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